The CSO announced in June 2018 that they are publishing a new set of data series called New Dwellings Completed. The purpose of this new data is to create realistic statistics on the number of new dwelling completions in Ireland.
Counting the number of new dwellings while important needs to be conducted in a wider context where factors that affect the reduction in the number of dwellings and demographic changes that affect demand for dwellings.
Focussing on the narrow issue of new dwellings may be a distraction on the wider problem of an increasing population and thus a greater demand for residential accommodation and changes that cause a reduction in dwellings.
There was virtually no net increase in the number of dwellings between the 2011 and 2016 census. There was a net increase of just 8,800 new dwellings while the population increased by 348,404.
Based on the assumption that around 85% of the number of planned units become actual units in 15 months, the current stock of planned residential units will translate roughly into just under 25,000 new units by mid-2019. This is a very small increase. This does not take into account any loss of residential housing stock during the same interval.
The purpose of this analysis is to assess trends in residential renting and rent prices.
This is a macro-level analysis based on a variety of publically available data sources.
The issue of prices for rental property in Ireland and, more particularly in Dublin and the other larger cities, and their rate of increase have dominated property-related discussions.
The data publically available on residential renting is patch, disparate and, in some case, of poor quality.
All the rent indices demonstrate the same pattern of increase.
Demand for rental properties is driven by increased population. A large part of the population increase in the renting age groups is due to net immigration who almost exclusively rent. It may also be that the recorded population number underestimates the actual population and thus the actual demand for rented accommodation
Most renters are aged between 25 to 44 – 60.7% in 2011 and 60.5% in 2016.
Nationally private landlords accounted for 68.0% of lettings in 2011 and 65.9% in 2016. In Dublin private landlords accounted 71.1% in 2011 and 69.3% in 2016.
RTB has records for 124,574 tenancies in the Dublin area with 272,981 bedrooms in March 2017. This excludes Local Authorities and a range of holiday, informal and family property lettings. The number of tenancies registered with the RTB has increased slightly indicating no drop in supply.
In the five and half years from Jun 2012 to Dec 2017, the number of BTL mortgages dropper by 27,821 or 18.52%. The number of repossessions in the interval was 4,897. So the number of BTL mortgages is dropping. This may be due to the group of people to who this lending relates – accidental landlords – selling their investment properties.
The last 10 years has seen the growth of the institutional residential property investor, especially in Dublin. Around 75% of large-scale multiple unit residential property purchases occurred in Dublin. These probably represent around 9,500 residential units. This represents a significant change in the rental sector, especially in Dublin.
There is a belief that residential institutional letters change a higher rent than other residential landlords. This may be one driver of increased rents.
In the last nine years, only 15,408 new property purchases were registered in Dublin. This illustrates the lack of new property supply in Dublin to accommodate a growing population and a demand for rental accommodation.
Airbnb rentals represent only 2.45% of the 124,574 registered tenancies and 2.08% of the 272,981 bedrooms in those tenancies and so is not significant.
Problems with availability and affordability of suitable residential rental accommodation represents a potential systemic economic risk.
Estimating The Size of the Irish PopulationAlan McSweeney
The various sources of population-related data are inconsistent with one another. There has been past issues with determining the extent of immigration. This in turn creates an issue with the size of the population of Ireland.
This analysis has identified one possible set of inconsistencies relating to the size of the Irish population. It may well be that the population of Ireland is greater that than counted by the CSO in the census.
Population sizes at various ages determine the demand for different societal resources. People are, after all, the direct and indirect buyers and users of products and services, both public and private sector. People drive demand. Changes in the profile of people – numbers and ages – will change the demand profile.
Discrepancies between other data sources from which population data can be inferred and the CSO’s population data indicate that there may be ongoing errors.
Consistency checking between multiple sets of related data is a standard technique to identify potential quality data issues that should then be the subject of further analysis. Detailed consistency checking is hampered by the limited set of information made publically available by various state agencies.
This analysis has looked at the following sets of data with a view to identifying potential data conflicts:
1. DEASP PPSN Registration Numbers
2. CSO PPSN Numbers
3. CSO Migration Numbers
4. CSO Population Numbers
5. Revenue Income Tax Numbers
6. Department of Education Third-Level Numbers
7. DEASP Pensioner Numbers
8. DEASP Live Register/Disability/Work Activation Numbers
9. Irish Naturalisation and Immigration Service (INIS) Statistics
Analysis of Irish Mortality Using Public Data Sources 2014-2020Alan McSweeney
This describes the use of published death notices on the web site www.rip.ie as a substitute to officially published mortality statistics. This analysis uses data from RIP.ie for the years 2014 to 2020.
Death notice information is available immediately and contains information at a greater level of detail than published statistics. There is a substantial lag in officially published mortality data.
Comparison of COVID-19 Mortality Data and Deaths for Ireland March 2020 – Mar...Alan McSweeney
This document compares published COVID-19 mortality statistics for Ireland with publicly available mortality data extracted from informal public data sources. This mortality data is taken from published death notices on the web site www.rip.ie. This is used a substitute for poor quality and long-delayed officially published mortality statistics.
Death notice information on the web site www.rip.ie is available immediately and contains information at a greater level of detail than published statistics. There is a substantial lag in officially published mortality data and the level of detail is very low. However, the extraction of death notice data and its conversion into a usable and accurate format requires a great deal of processing.
The objective of this analysis is to assess the accuracy of published COVID-19 mortality statistics by comparing trends in mortality over the years 2014 to 2020 with both numbers of deaths recorded from 2020 to 2021 and the COVID-19 statistics. It compares number of deaths for the seven 13-month intervals:
1. Mar 2014 - Mar 2015
2. Mar 2015 - Mar 2016
3. Mar 2016 - Mar 2017
4. Mar 2017 - Mar 2018
5. Mar 2018 - Mar 2019
6. Mar 2019 - Mar 2020
7. Mar 2020 - Mar 2021
It focuses on the seventh interval which is when COVID-19 deaths have occurred. It combines an analysis of mortality trends with details on COVID-19 deaths. This is a fairly simplistic analysis that looks to cross-check COVID-19 death statistics using data from other sources.
The subject of what constitutes a death from COVID-19 is controversial. This analysis is not concerned with addressing this controversy. It is concerned with comparing mortality data from a number of sources to identify potential discrepancies. It may be the case that while the total apparent excess number of deaths over an interval is less than the published number of COVID-19 deaths, the consequence of COVID-19 is to accelerate deaths that might have occurred later in the measurement interval.
Accurate data is needed to make informed decisions. Clearly there are issues with Irish COVID-19 mortality data. Accurate data is also needed to ensure public confidence in decision-making. Where this published data is inaccurate, this can lead of a loss of this confidence that can exploited.
Analysis of Possible Excess COVID-19 Deaths in Ireland From Jan 2020 to Jun 2020Alan McSweeney
This analysis seeks to determine if there are excess deaths that occurred in Ireland in the interval Jan – Jun 2020 that can be attributed to COVID-19. Excess deaths means deaths in excess of the number of expected deaths plus the number of deaths directly attributed to COVID-19. On the other hand a deficiency of deaths would occur when the number of expected deaths plus the number of deaths directly attributed to COVID-19 is less than the actual deaths.
This analysis uses number of deaths taken from the web site RIP.ie to generate an estimate of the number of deaths in Jan – Jun 2020 in the absence of any other official source. The last data extract from the RIP.ie web site was taken on 3 Jul 2020.
The analysis uses historical data from RIP.ie from 2018 and 2019 to assess its accuracy as a data source.
The analysis then uses the following three estimation approaches to assess the excess or deficiency of deaths:
1. The pattern of deaths in 2020 can be compared to previous comparable year or years. The additional COVID-19 deaths can be added to the comparable year and the difference between the expected, actual from RIP.ie and actual COVID-19 deaths can be analysed to generate an estimate of any excess or deficiency.
2. The age-specific mortality rates described on page 16 can be applied to estimates of population numbers to generates an estimate of expected deaths. This can be compared to the actual RIP.ie and actual COVID-19 deaths to generate an estimate of any excess or deficiency.
3. The range of death rates per 1,000 of population as described in Figure 10 on page 16 can be applied to estimates of population numbers to generates an estimate of expected deaths. This can be compared to the actual RIP.ie and actual COVID-19 deaths to generate an estimate of any excess or deficiency.
This analysis compares some data areas - Economy, Crime, Aviation, Energy, Transport, Health, Mortality. Housing and Construction - for Ireland for the years 2019 and 2020, illustrating the changes that have occurred between the two years. It shows some of the impacts of COVID-19 and of actions taken in response to it, such as the various lockdowns and other restrictions.
The first lockdown clearly had major changes on many aspects of Irish society. The third lockdown which began at the end of the period analysed will have as great an impact as the first lockdown.
The consequences of the events and actions that have causes these impacts could be felt for some time into the future.
Built a data warehouse from multiple data sources and ETL methodologies and executed three non-trivial Business Intelligence queries.
Technologies/Tools: R, SQL, Visual Studio, SQL Server Management, Tableau
In 2010, the Hungarian government led by Mr. Viktor Orbán has started the war on banks with a dual goal of filling state coffers with extra taxes and restoring majority local ownership in the banking sector, dominated by foreign players.
The purpose of this analysis is to assess trends in residential renting and rent prices.
This is a macro-level analysis based on a variety of publically available data sources.
The issue of prices for rental property in Ireland and, more particularly in Dublin and the other larger cities, and their rate of increase have dominated property-related discussions.
The data publically available on residential renting is patch, disparate and, in some case, of poor quality.
All the rent indices demonstrate the same pattern of increase.
Demand for rental properties is driven by increased population. A large part of the population increase in the renting age groups is due to net immigration who almost exclusively rent. It may also be that the recorded population number underestimates the actual population and thus the actual demand for rented accommodation
Most renters are aged between 25 to 44 – 60.7% in 2011 and 60.5% in 2016.
Nationally private landlords accounted for 68.0% of lettings in 2011 and 65.9% in 2016. In Dublin private landlords accounted 71.1% in 2011 and 69.3% in 2016.
RTB has records for 124,574 tenancies in the Dublin area with 272,981 bedrooms in March 2017. This excludes Local Authorities and a range of holiday, informal and family property lettings. The number of tenancies registered with the RTB has increased slightly indicating no drop in supply.
In the five and half years from Jun 2012 to Dec 2017, the number of BTL mortgages dropper by 27,821 or 18.52%. The number of repossessions in the interval was 4,897. So the number of BTL mortgages is dropping. This may be due to the group of people to who this lending relates – accidental landlords – selling their investment properties.
The last 10 years has seen the growth of the institutional residential property investor, especially in Dublin. Around 75% of large-scale multiple unit residential property purchases occurred in Dublin. These probably represent around 9,500 residential units. This represents a significant change in the rental sector, especially in Dublin.
There is a belief that residential institutional letters change a higher rent than other residential landlords. This may be one driver of increased rents.
In the last nine years, only 15,408 new property purchases were registered in Dublin. This illustrates the lack of new property supply in Dublin to accommodate a growing population and a demand for rental accommodation.
Airbnb rentals represent only 2.45% of the 124,574 registered tenancies and 2.08% of the 272,981 bedrooms in those tenancies and so is not significant.
Problems with availability and affordability of suitable residential rental accommodation represents a potential systemic economic risk.
Estimating The Size of the Irish PopulationAlan McSweeney
The various sources of population-related data are inconsistent with one another. There has been past issues with determining the extent of immigration. This in turn creates an issue with the size of the population of Ireland.
This analysis has identified one possible set of inconsistencies relating to the size of the Irish population. It may well be that the population of Ireland is greater that than counted by the CSO in the census.
Population sizes at various ages determine the demand for different societal resources. People are, after all, the direct and indirect buyers and users of products and services, both public and private sector. People drive demand. Changes in the profile of people – numbers and ages – will change the demand profile.
Discrepancies between other data sources from which population data can be inferred and the CSO’s population data indicate that there may be ongoing errors.
Consistency checking between multiple sets of related data is a standard technique to identify potential quality data issues that should then be the subject of further analysis. Detailed consistency checking is hampered by the limited set of information made publically available by various state agencies.
This analysis has looked at the following sets of data with a view to identifying potential data conflicts:
1. DEASP PPSN Registration Numbers
2. CSO PPSN Numbers
3. CSO Migration Numbers
4. CSO Population Numbers
5. Revenue Income Tax Numbers
6. Department of Education Third-Level Numbers
7. DEASP Pensioner Numbers
8. DEASP Live Register/Disability/Work Activation Numbers
9. Irish Naturalisation and Immigration Service (INIS) Statistics
Analysis of Irish Mortality Using Public Data Sources 2014-2020Alan McSweeney
This describes the use of published death notices on the web site www.rip.ie as a substitute to officially published mortality statistics. This analysis uses data from RIP.ie for the years 2014 to 2020.
Death notice information is available immediately and contains information at a greater level of detail than published statistics. There is a substantial lag in officially published mortality data.
Comparison of COVID-19 Mortality Data and Deaths for Ireland March 2020 – Mar...Alan McSweeney
This document compares published COVID-19 mortality statistics for Ireland with publicly available mortality data extracted from informal public data sources. This mortality data is taken from published death notices on the web site www.rip.ie. This is used a substitute for poor quality and long-delayed officially published mortality statistics.
Death notice information on the web site www.rip.ie is available immediately and contains information at a greater level of detail than published statistics. There is a substantial lag in officially published mortality data and the level of detail is very low. However, the extraction of death notice data and its conversion into a usable and accurate format requires a great deal of processing.
The objective of this analysis is to assess the accuracy of published COVID-19 mortality statistics by comparing trends in mortality over the years 2014 to 2020 with both numbers of deaths recorded from 2020 to 2021 and the COVID-19 statistics. It compares number of deaths for the seven 13-month intervals:
1. Mar 2014 - Mar 2015
2. Mar 2015 - Mar 2016
3. Mar 2016 - Mar 2017
4. Mar 2017 - Mar 2018
5. Mar 2018 - Mar 2019
6. Mar 2019 - Mar 2020
7. Mar 2020 - Mar 2021
It focuses on the seventh interval which is when COVID-19 deaths have occurred. It combines an analysis of mortality trends with details on COVID-19 deaths. This is a fairly simplistic analysis that looks to cross-check COVID-19 death statistics using data from other sources.
The subject of what constitutes a death from COVID-19 is controversial. This analysis is not concerned with addressing this controversy. It is concerned with comparing mortality data from a number of sources to identify potential discrepancies. It may be the case that while the total apparent excess number of deaths over an interval is less than the published number of COVID-19 deaths, the consequence of COVID-19 is to accelerate deaths that might have occurred later in the measurement interval.
Accurate data is needed to make informed decisions. Clearly there are issues with Irish COVID-19 mortality data. Accurate data is also needed to ensure public confidence in decision-making. Where this published data is inaccurate, this can lead of a loss of this confidence that can exploited.
Analysis of Possible Excess COVID-19 Deaths in Ireland From Jan 2020 to Jun 2020Alan McSweeney
This analysis seeks to determine if there are excess deaths that occurred in Ireland in the interval Jan – Jun 2020 that can be attributed to COVID-19. Excess deaths means deaths in excess of the number of expected deaths plus the number of deaths directly attributed to COVID-19. On the other hand a deficiency of deaths would occur when the number of expected deaths plus the number of deaths directly attributed to COVID-19 is less than the actual deaths.
This analysis uses number of deaths taken from the web site RIP.ie to generate an estimate of the number of deaths in Jan – Jun 2020 in the absence of any other official source. The last data extract from the RIP.ie web site was taken on 3 Jul 2020.
The analysis uses historical data from RIP.ie from 2018 and 2019 to assess its accuracy as a data source.
The analysis then uses the following three estimation approaches to assess the excess or deficiency of deaths:
1. The pattern of deaths in 2020 can be compared to previous comparable year or years. The additional COVID-19 deaths can be added to the comparable year and the difference between the expected, actual from RIP.ie and actual COVID-19 deaths can be analysed to generate an estimate of any excess or deficiency.
2. The age-specific mortality rates described on page 16 can be applied to estimates of population numbers to generates an estimate of expected deaths. This can be compared to the actual RIP.ie and actual COVID-19 deaths to generate an estimate of any excess or deficiency.
3. The range of death rates per 1,000 of population as described in Figure 10 on page 16 can be applied to estimates of population numbers to generates an estimate of expected deaths. This can be compared to the actual RIP.ie and actual COVID-19 deaths to generate an estimate of any excess or deficiency.
This analysis compares some data areas - Economy, Crime, Aviation, Energy, Transport, Health, Mortality. Housing and Construction - for Ireland for the years 2019 and 2020, illustrating the changes that have occurred between the two years. It shows some of the impacts of COVID-19 and of actions taken in response to it, such as the various lockdowns and other restrictions.
The first lockdown clearly had major changes on many aspects of Irish society. The third lockdown which began at the end of the period analysed will have as great an impact as the first lockdown.
The consequences of the events and actions that have causes these impacts could be felt for some time into the future.
Built a data warehouse from multiple data sources and ETL methodologies and executed three non-trivial Business Intelligence queries.
Technologies/Tools: R, SQL, Visual Studio, SQL Server Management, Tableau
In 2010, the Hungarian government led by Mr. Viktor Orbán has started the war on banks with a dual goal of filling state coffers with extra taxes and restoring majority local ownership in the banking sector, dominated by foreign players.
A Banking Perspective on Historic Tax Credits - Michael TaylorHeritage Ohio
Michael Taylor of PNC Bank discusses the banking perspective on historic tax credits at the Heritage Ohio Historic Tax Credit Workshop in Toledo, Ohio on March 25, 2011
Rollits Planning Law and Policy Newsletter - February 2019 Pat Coyle
Legal newsletter covering topics such as permitted development rights on agricultural land, Class A permitted development rights, CIL and a planning policy update.
This white paper can help tax professionals understand the challenges of managing fixed assets involved in a technical termination and how to more efficiently and accurately handle the set-up, transfer, and management of those assets.
India - Renewables - eligible overseas capital markets candidate 2015Varun Sethi
India Renewables : Ready for a multi year, recurring, Capital Markets (IPO) activity.
There is fad and hype around a virtual world being created by technology companies including consumer internet, SaaS, IoT, Big Data, Social/Mobile commerce and billions have been invested into it and more lined up.
A silent (so far) revolution has been pioneered for Indian renewable companies (RC) which is no less than the fad and hype of the technology sector. RCs need to explore sustainable renewable energy finance with IPOs, innovative structures like Yield Cos (Utility and commercial scale plants) as also investment plans (for residential solar - My power loans)
The presentation essentially summarizes the renewables eco system in India, global solar experiences (US), IFRS/ US GAAP A/C issues for renewables sector, future of solar n grid parity, US IPO concepts n regulatory environment.
India Renewables-Eligible Overseas Capital Markets CandidateVarun Sethi
A silent (so far) revolution has been pioneered for Indian renewable companies (RC) which is no less than the fad and hype of the technology sector. RCs need to explore sustainable renewable energy finance with IPOs, innovative structures like Yield Cos (Utility and commercial scale plants) as also investment plans (for residential solar - My power loans)
The presentation essentially summarizes the renewables eco system in India, global solar experiences (US), IFRS/ US GAAP A/C issues for renewables sector, future of solar n grid parity, US IPO concepts n regulatory environment.
A silent (so far) revolution has been pioneered for Indian renewable companies (RC) which is no less than the fad and hype of the technology sector. RCs need to explore sustainable renewable energy finance with IPOs, innovative structures like Yield Cos (Utility and commercial scale plants) as also investment plans (for residential solar - My power loans)
The presentation essentially summarizes the renewables eco system in India, global solar experiences (US), IFRS/ US GAAP A/C issues for renewables sector, future of solar n grid parity, US IPO concepts n regulatory environment.
The UK electorate’s 52/48 vote to leave the European Union has caused uncertainty in markets, with property investment one of the sectors generating the most headlines. Aon Hewitt Partner Nick Duff provides our lead story ‘Brexit and the immediate aftermath’ with some practical observations. He suggests despite some pressure on valuations, the UK property market is likely to hold up owing to its attractiveness to long term investors.
The data architecture of solutions is frequently not given the attention it deserves or needs. Frequently, too little attention is paid to designing and specifying the data architecture within individual solutions and their constituent components. This is due to the behaviours of both solution architects ad data architects.
Solution architecture tends to concern itself with functional, technology and software components of the solution
Data architecture tends not to get involved with the data aspects of technology solutions, leaving a data architecture gap. Combined with the gap where data architecture tends not to get involved with the data aspects of technology solutions, there is also frequently a solution architecture data gap. Solution architecture also frequently omits the detail of data aspects of solutions leading to a solution data architecture gap. These gaps result in a data blind spot for the organisation.
Data architecture tends to concern itself with post-individual solutions. Data architecture needs to shift left into the domain of solutions and their data and more actively engage with the data dimensions of individual solutions. Data architecture can provide the lead in sealing these data gaps through a shift-left of its scope and activities as well providing standards and common data tooling for solution data architecture
The objective of data design for solutions is the same as that for overall solution design:
• To capture sufficient information to enable the solution design to be implemented
• To unambiguously define the data requirements of the solution and to confirm and agree those requirements with the target solution consumers
• To ensure that the implemented solution meets the requirements of the solution consumers and that no deviations have taken place during the solution implementation journey
Solution data architecture avoids problems with solution operation and use:
• Poor and inconsistent data quality
• Poor performance, throughput, response times and scalability
• Poorly designed data structures can lead to long data update times leading to long response times, affecting solution usability, loss of productivity and transaction abandonment
• Poor reporting and analysis
• Poor data integration
• Poor solution serviceability and maintainability
• Manual workarounds for data integration, data extract for reporting and analysis
Data-design-related solution problems frequently become evident and manifest themselves only after the solution goes live. The benefits of solution data architecture are not always evident initially.
Solution Architecture and Solution Estimation.pdfAlan McSweeney
Solution architects and the solution architecture function are ideally placed to create solution delivery estimates
Solution architects have the knowledge and understanding of the solution constituent component and structure that is needed to create solution estimate:
• Knowledge of solution options
• Knowledge of solution component structure to define a solution breakdown structure
• Knowledge of available components and the options for reuse
• Knowledge of specific solution delivery constraints and standards that both control and restrain solution options
Accurate solution delivery estimates are need to understand the likely cost/resources/time/options needed to implement a new solution within the context of a range of solutions and solution options. These estimates are a key input to investment management and making effective decisions on the portfolio of solutions to implement. They enable informed decision-making as part of IT investment management.
An estimate is not a single value. It is a range of values depending on a number of conditional factors such level of knowledge, certainty, complexity and risk. The range will narrow as the level of knowledge and uncertainty decreases
There is no easy or magic way to create solution estimates. You have to engage with the complexity of the solution and its components. The more effort that is expended the more accurate the results of the estimation process will be. But there is always a need to create estimates (reasonably) quickly so a balance is needed between effort and quality of results.
The notes describe a structured solution estimation process and an associated template. They also describe the wider context of solution estimates in terms of IT investment and value management and control.
More Related Content
Similar to How Many Net New Residential Units Are Really Available In Ireland?
A Banking Perspective on Historic Tax Credits - Michael TaylorHeritage Ohio
Michael Taylor of PNC Bank discusses the banking perspective on historic tax credits at the Heritage Ohio Historic Tax Credit Workshop in Toledo, Ohio on March 25, 2011
Rollits Planning Law and Policy Newsletter - February 2019 Pat Coyle
Legal newsletter covering topics such as permitted development rights on agricultural land, Class A permitted development rights, CIL and a planning policy update.
This white paper can help tax professionals understand the challenges of managing fixed assets involved in a technical termination and how to more efficiently and accurately handle the set-up, transfer, and management of those assets.
India - Renewables - eligible overseas capital markets candidate 2015Varun Sethi
India Renewables : Ready for a multi year, recurring, Capital Markets (IPO) activity.
There is fad and hype around a virtual world being created by technology companies including consumer internet, SaaS, IoT, Big Data, Social/Mobile commerce and billions have been invested into it and more lined up.
A silent (so far) revolution has been pioneered for Indian renewable companies (RC) which is no less than the fad and hype of the technology sector. RCs need to explore sustainable renewable energy finance with IPOs, innovative structures like Yield Cos (Utility and commercial scale plants) as also investment plans (for residential solar - My power loans)
The presentation essentially summarizes the renewables eco system in India, global solar experiences (US), IFRS/ US GAAP A/C issues for renewables sector, future of solar n grid parity, US IPO concepts n regulatory environment.
India Renewables-Eligible Overseas Capital Markets CandidateVarun Sethi
A silent (so far) revolution has been pioneered for Indian renewable companies (RC) which is no less than the fad and hype of the technology sector. RCs need to explore sustainable renewable energy finance with IPOs, innovative structures like Yield Cos (Utility and commercial scale plants) as also investment plans (for residential solar - My power loans)
The presentation essentially summarizes the renewables eco system in India, global solar experiences (US), IFRS/ US GAAP A/C issues for renewables sector, future of solar n grid parity, US IPO concepts n regulatory environment.
A silent (so far) revolution has been pioneered for Indian renewable companies (RC) which is no less than the fad and hype of the technology sector. RCs need to explore sustainable renewable energy finance with IPOs, innovative structures like Yield Cos (Utility and commercial scale plants) as also investment plans (for residential solar - My power loans)
The presentation essentially summarizes the renewables eco system in India, global solar experiences (US), IFRS/ US GAAP A/C issues for renewables sector, future of solar n grid parity, US IPO concepts n regulatory environment.
The UK electorate’s 52/48 vote to leave the European Union has caused uncertainty in markets, with property investment one of the sectors generating the most headlines. Aon Hewitt Partner Nick Duff provides our lead story ‘Brexit and the immediate aftermath’ with some practical observations. He suggests despite some pressure on valuations, the UK property market is likely to hold up owing to its attractiveness to long term investors.
Similar to How Many Net New Residential Units Are Really Available In Ireland? (20)
The data architecture of solutions is frequently not given the attention it deserves or needs. Frequently, too little attention is paid to designing and specifying the data architecture within individual solutions and their constituent components. This is due to the behaviours of both solution architects ad data architects.
Solution architecture tends to concern itself with functional, technology and software components of the solution
Data architecture tends not to get involved with the data aspects of technology solutions, leaving a data architecture gap. Combined with the gap where data architecture tends not to get involved with the data aspects of technology solutions, there is also frequently a solution architecture data gap. Solution architecture also frequently omits the detail of data aspects of solutions leading to a solution data architecture gap. These gaps result in a data blind spot for the organisation.
Data architecture tends to concern itself with post-individual solutions. Data architecture needs to shift left into the domain of solutions and their data and more actively engage with the data dimensions of individual solutions. Data architecture can provide the lead in sealing these data gaps through a shift-left of its scope and activities as well providing standards and common data tooling for solution data architecture
The objective of data design for solutions is the same as that for overall solution design:
• To capture sufficient information to enable the solution design to be implemented
• To unambiguously define the data requirements of the solution and to confirm and agree those requirements with the target solution consumers
• To ensure that the implemented solution meets the requirements of the solution consumers and that no deviations have taken place during the solution implementation journey
Solution data architecture avoids problems with solution operation and use:
• Poor and inconsistent data quality
• Poor performance, throughput, response times and scalability
• Poorly designed data structures can lead to long data update times leading to long response times, affecting solution usability, loss of productivity and transaction abandonment
• Poor reporting and analysis
• Poor data integration
• Poor solution serviceability and maintainability
• Manual workarounds for data integration, data extract for reporting and analysis
Data-design-related solution problems frequently become evident and manifest themselves only after the solution goes live. The benefits of solution data architecture are not always evident initially.
Solution Architecture and Solution Estimation.pdfAlan McSweeney
Solution architects and the solution architecture function are ideally placed to create solution delivery estimates
Solution architects have the knowledge and understanding of the solution constituent component and structure that is needed to create solution estimate:
• Knowledge of solution options
• Knowledge of solution component structure to define a solution breakdown structure
• Knowledge of available components and the options for reuse
• Knowledge of specific solution delivery constraints and standards that both control and restrain solution options
Accurate solution delivery estimates are need to understand the likely cost/resources/time/options needed to implement a new solution within the context of a range of solutions and solution options. These estimates are a key input to investment management and making effective decisions on the portfolio of solutions to implement. They enable informed decision-making as part of IT investment management.
An estimate is not a single value. It is a range of values depending on a number of conditional factors such level of knowledge, certainty, complexity and risk. The range will narrow as the level of knowledge and uncertainty decreases
There is no easy or magic way to create solution estimates. You have to engage with the complexity of the solution and its components. The more effort that is expended the more accurate the results of the estimation process will be. But there is always a need to create estimates (reasonably) quickly so a balance is needed between effort and quality of results.
The notes describe a structured solution estimation process and an associated template. They also describe the wider context of solution estimates in terms of IT investment and value management and control.
Validating COVID-19 Mortality Data and Deaths for Ireland March 2020 – March ...Alan McSweeney
This analysis seeks to validate published COVID-19 mortality statistics using mortality data derived from general mortality statistics, mortality estimated from population size and mortality rates and death notice data
Analysis of the Numbers of Catholic Clergy and Members of Religious in Irelan...Alan McSweeney
This analysis looks at the changes in the numbers of priests and nuns in Ireland for the years 1926 to 2016. It combines data from a range of sources to show the decline in the numbers of priests and nuns and their increasing age profile.
This analysis consists of the following sections:
• Summary - this highlights some of the salient points in the analysis.
• Overview of Analysis - this describes the approach taken in this analysis.
• Context – this provides background information on the number of Catholics in Ireland as a context to this analysis.
• Analysis of Census Data 1926 – 2016 - this analyses occupation age profile data for priests and nuns. It also includes sample projections on the numbers of priests and nuns.
• Analysis of Catholic Religious Mortality 2014-2021 - this analyses death notice data from RIP.ie to shows the numbers of priests and nuns that have died in the years 2014 to 2021. It also looks at deaths of Irish priests and nuns outside Ireland and at the numbers of countries where Irish priests and nuns have worked.
• Analysis of Data on Catholic Clergy From Other Sources - this analyses data on priests and nuns from other sources.
• Notes on Data Sources and Data Processing - this lists the data sources used in this analysis.
IT Architecture’s Role In Solving Technical Debt.pdfAlan McSweeney
Technical debt is an overworked term without an effective and common agreed understanding of what exactly it is, what causes it, what are its consequences, how to assess it and what to do about it.
Technical debt is the sum of additional direct and indirect implementation and operational costs incurred and risks and vulnerabilities created because of sub-optimal solution design and delivery decisions.
Technical debt is the sum of all the consequences of all the circumventions, budget reduction, time pressure, lack of knowledge, manual workarounds, short-cuts, avoidance, poor design and delivery quality and decisions to remove elements from solution scope and failure to provide foundational and backbone solution infrastructure.
Technical debt leads to a negative feedback cycle with short solution lifespan, earlier solution replacement and short-term tactical remedial actions.
All the disciplines within IT architecture have a role to play in promoting an understanding of and in the identification of how to resolve technical debt. IT architecture can provide the leadership in both remediating existing technical debt and preventing future debt.
Failing to take a complete view of the technical debt within the organisation means problems and risks remained unrecognised and unaddressed. The real scope of the problem is substantially underestimated. Technical debt is always much more than poorly written software.
Technical debt can introduce security risks and vulnerabilities into the organisation’s solution landscape. Failure to address technical debt leaves exploitable security risks and vulnerabilities in place.
Shadow IT or ghost IT is a largely unrecognised source of technical debt including security risks and vulnerabilities. Shadow IT is the consequence of a set of reactions by business functions to an actual or perceived inability or unwillingness of the IT function to respond to business needs for IT solutions. Shadow IT is frequently needed to make up for gaps in core business solutions, supplementing incomplete solutions and providing omitted functionality.
Solution Architecture And Solution SecurityAlan McSweeney
This describes an approach to embedding security within the technology solution landscape. It describes a security model that encompasses the range of individual solution components up to the entire solution landscape. The solution security model allows the security status of a solution and its constituent delivery and operational components to be tracked wherever those components are located. This provides an integrated approach to solution security across all solution components and across the entire organisation topology of solutions. It allows the solution architect to validate the security of an individual solution. It enables the security status of the entire solution landscape to be assessed and recorded. Solution security is a wicked problem because there is no certainly about when the problem has been resolved and a state of security has been achieved. The security state of a solution can just be expressed along a subjective spectrum of better or worse rather than a binary true or false. Solution security can have negative consequences: prevents types of access, limits availability in different ways, restricts functionality provided, makes solution harder to use, lengthens solution delivery times, increases costs along the entire solution lifecycle, leads to loss of usability, utility and rate of use.
Data Privatisation, Data Anonymisation, Data Pseudonymisation and Differentia...Alan McSweeney
This paper describes how technologies such as data pseudonymisation and differential privacy technology enables access to sensitive data and unlocks data opportunities and value while ensuring compliance with data privacy legislation and regulations.
Data Privatisation, Data Anonymisation, Data Pseudonymisation and Differentia...Alan McSweeney
Your data has value to your organisation and to relevant data sharing partners. It has been expensively obtained. It represents a valuable asset on which a return must be generated. To achieve the value inherent in the data you need to be able to make it appropriately available to others, both within and outside the organisation.
Organisations are frequently data rich and information poor, lacking the skills, experience and resources to convert raw data into value.
These notes outline technology approaches to achieving compliance with data privacy regulations and legislation while providing access to data.
There are different routes to making data accessible and shareable within and outside the organisation without compromising compliance with data protection legislation and regulations and removing the risk associated with allowing access to personal data:
• Differential Privacy – source data is summarised and individual personal references are removed. The one-to-one correspondence between original and transformed data has been removed
• Anonymisation – identifying data is destroyed and cannot be recovered so individual cannot be identified. There is still a one-to-one correspondence between original and transformed data
• Pseudonymisation – identifying data is encrypted and recovery data/token is stored securely elsewhere. There is still a one-to-one correspondence between original and transformed data
These technologies and approaches are not mutually exclusive – each is appropriate to differing data sharing and data access use cases
The data privacy regulatory and legislative landscape is complex and getting even more complex so an approach to data access and sharing that embeds compliance as a matter of course is required.
Appropriate technology appropriately implemented and operated is a means of managing and reducing risks of re-identification by making the time, skills, resources and money necessary to achieve this unrealistic.
Technology is part of a risk management approach to data privacy. There is wider operational data sharing and data privacy framework that includes technology aspects, among other key areas. Using these technologies will embed such compliance by design into your data sharing and access facilities. This will allow you to realise value from your data successfully.
Solution architects must be aware of the need for solution security and of the need to have enterprise-level controls that solutions can adopt.
The sets of components that comprise the extended solution landscape, including those components that provide common or shared functionality, are located in different zones, each with different security characteristics.
The functional and operational design of any solution and therefore its security will include many of these components, including those inherited by the solution or common components used by the solution.
The complete solution security view should refer explicitly to the components and their controls.
While each individual solution should be able to inherit the security controls provided by these components, the solution design should include explicit reference to them for completeness and to avoid unvalidated assumptions.
There is a common and generalised set of components, many of which are shared, within the wider solution topology that should be considered when assessing overall solution architecture and solution security.
Individual solutions must be able to inherit security controls, facilities and standards from common enterprise-level controls, standards, toolsets and frameworks.
Individual solutions must not be forced to implement individual infrastructural security facilities and controls. This is wasteful of solution implementation resources, results in multiple non-standard approaches to security and represents a security risk to the organisation.
The extended solution landscape potentially consists of a large number of interacting components and entities located in different zones, each with different security profiles, requirements and concerns. Different security concerns and therefore controls apply to each of these components.
Solution security is not covered by a single control. It involves multiple overlapping sets of controls providing layers of security.
Solution Architecture And (Robotic) Process Automation SolutionsAlan McSweeney
Automation is a technology trend IT architects should be aware of and know how to respond to business requests as well as recommend automation technologies and solutions where appropriate. Automation is a bigger topic than just RPA (Robotic Process Automation).
Automation solutions, like all other technology solutions, should be subject to an architecture and design process. There are many approaches to and options for the automation of business activities. Too often automation solutions are tactical applications layered over existing business systems
The objective of all IT solutions is to automate manual business processes and their activities to a certain extent. The requirement for RPA-type applications arises in part because of automation failures within existing applications or the need to automate the interactions with or integrations between separate, possibly legacy, applications.
One of the roles of IT architecture is to always seek to take the wider architectural view and to ensure that solutions are designed and delivered within a strategic framework to avoid, as much as is practical and realistic, short-term tactical solutions and approaches that lead to an accumulation of design, operations and support debt. Tactical solutions will always play a part in the organisation’s solution landscape.
The objective of these notes is to put automation into its wider and larger IT architecture context while accepting the need for tactical approaches in some instances.
These notes cover the following topics:
• Solution And Process Automation – The Wider Technology And Approach Landscape
• Business Processes, Business Solutions And Automation
• Organisation Process Model
• Strategic And Tactical Automation
• Deciding On The Scope Of Automation
• Digital Strategy, Digital Transformation And Automation
• Specifying The Automation Solution
• Business Process Model and Notation (BPMN)
• Sample Business Process – Order To Cash
• RPA (Robotic Process Automation)
Data Profiling, Data Catalogs and Metadata HarmonisationAlan McSweeney
These notes discuss the related topics of Data Profiling, Data Catalogs and Metadata Harmonisation. It describes a detailed structure for data profiling activities. It identifies various open source and commercial tools and data profiling algorithms. Data profiling is a necessary pre-requisite activity in order to construct a data catalog. A data catalog makes an organisation’s data more discoverable. The data collected during data profiling forms the metadata contained in the data catalog. This assists with ensuring data quality. It is also a necessary activity for Master Data Management initiatives. These notes describe a metadata structure and provide details on metadata standards and sources.
Analysis of Decentralised, Distributed Decision-Making For Optimising Domesti...Alan McSweeney
This analysis looks at the potential impact that large numbers of electric vehicles could have on electricity demand, electricity generation capacity and on the electricity transmission and distribution grid in Ireland. It combines data from a number of sources – electricity usage patterns, vehicle usage patterns, electric vehicle current and possible future market share – to assess the potential impact of electric vehicles.
It then analyses a possible approach to electric vehicle charging where the domestic charging unit has some degree of decentralised intelligence and decision-making capability in deciding when to start vehicle charging to minimise electricity usage impact and optimise electricity generation usage.
The potential problem to be addressed is that if large numbers of electric cars are plugged-in and charging starts immediately when the drivers of those cars arrive home, the impact on demand for electricity will be substantial.
Operational Risk Management Data Validation ArchitectureAlan McSweeney
This describes a structured approach to validating data used to construct and use an operational risk model. It details an integrated approach to operational risk data involving three components:
1. Using the Open Group FAIR (Factor Analysis of Information Risk) risk taxonomy to create a risk data model that reflects the required data needed to assess operational risk
2. Using the DMBOK model to define a risk data capability framework to assess the quality and accuracy of risk data
3. Applying standard fault analysis approaches - Fault Tree Analysis (FTA) and Failure Mode and Effect Analysis (FMEA) - to the risk data capability framework to understand the possible causes of risk data failures within the risk model definition, operation and use
Data Integration, Access, Flow, Exchange, Transfer, Load And Extract Architec...Alan McSweeney
These notes describe a generalised data integration architecture framework and set of capabilities.
With many organisations, data integration tends to have evolved over time with many solution-specific tactical approaches implemented. The consequence of this is that there is frequently a mixed, inconsistent data integration topography. Data integrations are often poorly understood, undocumented and difficult to support, maintain and enhance.
Data interoperability and solution interoperability are closely related – you cannot have effective solution interoperability without data interoperability.
Data integration has multiple meanings and multiple ways of being used such as:
- Integration in terms of handling data transfers, exchanges, requests for information using a variety of information movement technologies
- Integration in terms of migrating data from a source to a target system and/or loading data into a target system
- Integration in terms of aggregating data from multiple sources and creating one source, with possibly date and time dimensions added to the integrated data, for reporting and analytics
- Integration in terms of synchronising two data sources or regularly extracting data from one data sources to update a target
- Integration in terms of service orientation and API management to provide access to raw data or the results of processing
There are two aspects to data integration:
1. Operational Integration – allow data to move from one operational system and its data store to another
2. Analytic Integration – move data from operational systems and their data stores into a common structure for analysis
Ireland 2019 and 2020 Compared - Individual ChartsAlan McSweeney
This analysis compares some data areas - Economy, Crime, Aviation, Energy, Transport, Health, Mortality. Housing and Construction - for Ireland for the years 2019 and 2020, illustrating the changes that have occurred between the two years. It shows some of the impacts of COVID-19 and of actions taken in response to it, such as the various lockdowns and other restrictions.
The first lockdown clearly had major changes on many aspects of Irish society. The third lockdown which began at the end of the period analysed will have as great an impact as the first lockdown.
The consequences of the events and actions that have causes these impacts could be felt for some time into the future.
Review of Information Technology Function Critical Capability ModelsAlan McSweeney
IT Function critical capabilities are key areas where the IT function needs to maintain significant levels of competence, skill and experience and practise in order to operate and deliver a service. There are several different IT capability frameworks. The objective of these notes is to assess the suitability and applicability of these frameworks. These models can be used to identify what is important for your IT function based on your current and desired/necessary activity profile.
Capabilities vary across organisation – not all capabilities have the same importance for all organisations. These frameworks do not readily accommodate variability in the relative importance of capabilities.
The assessment approach taken is to identify a generalised set of capabilities needed across the span of IT function operations, from strategy to operations and delivery. This generic model is then be used to assess individual frameworks to determine their scope and coverage and to identify gaps.
The generic IT function capability model proposed here consists of five groups or domains of major capabilities that can be organised across the span of the IT function:
1. Information Technology Strategy, Management and Governance
2. Technology and Platforms Standards Development and Management
3. Technology and Solution Consulting and Delivery
4. Operational Run The Business/Business as Usual/Service Provision
5. Change The Business/Development and Introduction of New Services
In the context of trends and initiatives such as outsourcing, transition to cloud services and greater platform-based offerings, should the IT function develop and enhance its meta-capabilities – the management of the delivery of capabilities? Is capability identification and delivery management the most important capability? Outsourced service delivery in all its forms is not a fire-and-forget activity. You can outsource the provision of any service except the management of the supply of that service.
The following IT capability models have been evaluated:
• IT4IT Reference Architecture https://www.opengroup.org/it4it contains 32 functional components
• European e-Competence Framework (ECF) http://www.ecompetences.eu/ contains 40 competencies
• ITIL V4 https://www.axelos.com/best-practice-solutions/itil has 34 management practices
• COBIT 2019 https://www.isaca.org/resources/cobit has 40 management and control processes
• APQC Process Classification Framework - https://www.apqc.org/process-performance-management/process-frameworks version 7.2.1 has 44 major IT management processes
• IT Capability Maturity Framework (IT-CMF) https://ivi.ie/critical-capabilities/ contains 37 critical capabilities
The following model has not been evaluated
• Skills Framework for the Information Age (SFIA) - http://www.sfia-online.org/ lists over 100 skills
Critical Review of Open Group IT4IT Reference ArchitectureAlan McSweeney
This reviews the Open Group’s IT4IT Reference Architecture (https://www.opengroup.org/it4it) with respect to other operational frameworks to determine its suitability and applicability to the IT operating function.
IT4IT is intended to be a reference architecture for the management of the IT function. It aims to take a value chain approach to create a model of the functions that IT performs and the services it provides to assist organisations in the identification of the activities that contribute to business competitiveness. It is intended to be an integrated framework for the management of IT that emphasises IT service lifecycles.
This paper reviews what is meant by a value-chain, with special reference to the Supply Chain Operations Reference (SCOR) model (https://www.apics.org/apics-for-business/frameworks/scor). the most widely used and most comprehensive such model.
The SCOR model is part of wider set of operations reference models that describe a view of the critical elements in a value chain:
• Product Life Cycle Operations Reference model (PLCOR) - Manages the activities for product innovation and product and portfolio management
• Customer Chain Operations Reference model (CCOR) - Manages the customer interaction processes
• Design Chain Operations Reference model (DCOR) - Manages the product and service development processes
• Managing for Supply Chain Performance (M4SC) - Translates business strategies into supply chain execution plans and policies
It also compares the IT4IT Reference Architecture and its 32 functional components to other frameworks that purport to identify the critical capabilities of the IT function:
• IT Capability Maturity Framework (IT-CMF) https://ivi.ie/critical-capabilities/ contains 37 critical capabilities
• Skills Framework for the Information Age (SFIA) - http://www.sfia-online.org/ lists over 100 skills
• European e-Competence Framework (ECF) http://www.ecompetences.eu/ contains 40 competencies
• ITIL IT Service Management https://www.axelos.com/best-practice-solutions/itil
• COBIT 2019 https://www.isaca.org/resources/cobit has 40 management and control processes
This presentation describes systematic, repeatable and co-ordinated approach to agile solution architecture and design. It is intended to describe a set of practical steps and activities embedded within a framework to allow an agile method to be adopted and used for solution design and delivery. This approach ensures consistency in the assessment of solution design options and in subsequent solution design and solution delivery activities. This process leads to the rapid design and delivery of realistic and achievable solutions that meet real solution consumer needs. The approach provides for effective solution decision-making. It generates options and results quickly and consistently. Implementing a framework such as this provides for the creation of a knowledgebase of previous solution design and delivery exercises that leads to an accumulated body of knowledge within the organisation.
Solution Architecture and Solution AcquisitionAlan McSweeney
This describes a systematised and structured approach to solution acquisition or procurement that involves solution architecture from the start. This allows the true scope of both the required and subsequently acquired solution are therefore fully understood. By using such an approach, poor solution acquisition outcomes are avoided.
Solution architecture provides the structured approach to capturing all the cost contributors and knowing the true solution scope.
There is more packaged/product/service-based solution acquisition activity. There is an increasing trend of solutions hosted outside the organisation. Meanwhile solution acquisition outcomes are poor and getting worse.
Poor solution acquisition has long-term consequences and costs.
The to-be-acquired solution needs to operate in and co-exist with an existing solution topography and the solution acquisition process needs to be aware of and take account of this wider solution topography. Cloud-based or externally hosted and provided solutions do not eliminate the need for the solution to exist within the organisation solution topography.
Strategic misrepresentation in solution acquisition is the deliberate distortion or falsification of information relating to solution acquisition costs, complexity, required functionality, solution availability, resource availability, time to implement in order to get solution acquisition approval. Strategic misrepresentation is very real and its consequences can be very damaging.
Solution architecture has the skills and experience to define the real scope of the solution being acquired. An effective structured solution acquisition process, well-implemented and consistently applied, means dependable and repeatable solution acquisition and successful outcomes.
Creating A Business Focussed Information Technology StrategyAlan McSweeney
This presentation describes a structured approach to creating a business-focussed information technology strategy.
An effective business-oriented IT strategy is an opportunity to resolve the disconnection and to ensure the IT function is able to and does respond to business needs and is trusted by the business to provide IT solutions.
The IT strategy will consist of static structural elements relating to the organisation of the IT function:
• Capabilities – skills and abilities the IT function should possess and be able to use effectively and efficiently
• IT Function Structure – the organisation and arrangement of the sub-functions and their responsibilities and relationships
• Operating Model – how the IT function work and delivers value and the processes it implements and operates
• Staffing And Roles – the numbers of people, their roles, responsibilities, expected skills, experience and abilities, workload, reporting structures and expected ways of operating
It will also include dynamic elements relating to initiatives, both enabling initiatives within the IT function and specific business initiatives required to achieve the business strategy.
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
Analysis insight about a Flyball dog competition team's performanceroli9797
Insight of my analysis about a Flyball dog competition team's last year performance. Find more: https://github.com/rolandnagy-ds/flyball_race_analysis/tree/main
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
How Many Net New Residential Units Are Really Available In Ireland?
1. How Many Net New
Residential Units Are
Really Available In
Ireland?
Notes on Recently Published CSO New
Dwellings Completed Numbers and a Wider
Review of Demographic Factors
Alan McSweeney
June 2018
http://ie.linkedin.com/in/alanmcsweeney
2. How Many Net New Residential Units Are Really Available?
Page 2
Contents
Introduction.......................................................................................................................................... 3
What Needs To Be Measured? ............................................................................................................... 3
Number Of New Dwellings And Census Data......................................................................................... 9
Summary............................................................................................................................................. 12
3. How Many Net New Residential Units Are Really Available?
Page 3
Introduction
The CSO announced in June 20181 that they are publishing a new set of data series called New Dwellings
Completed2. The purpose of this new data is to create realistic statistics on the number of new dwelling
completions in Ireland.
The previous estimates were based on new connections to the electricity network. It was widely
acknowledged that this overestimated the number of new residential dwellings being built. It also did
not include commercial premises that were converted to residential use.
The purpose of this note is to examine this new data in the context of estimating the new number of
newly available residential dwellings needed to accommodate demand caused by demographic changes.
What Needs To Be Measured?
The first issue to be considered is what are the policy questions that need to be (asked and) answered and
what information is needed to provide answers?
The objective should be to understand the demand for residential accommodation – both purchase and
rental in the context of demand.
Counting the number of newly built residential dwellings is just one aspect of understanding the state of
the residential property stock.
A more complete analysis requires taking data from a wider range of sources to create a more informed
view of the state of residential property in Ireland.
Schematically, the sequence and flow of key sets of measures of property-related activities is:
1https://www.cso.ie/en/releasesandpublications/ep/p-ndc/newdwellingcompletionsq12018/overview/
2https://www.cso.ie/px/pxeirestat/Database/eirestat/New%20Dwelling%20Completions/New%20Dwelling%20Com
pletions_statbank.asp
4. How Many Net New Residential Units Are Really Available?
Page 4
These activities are:
1. Planning Permissions for New Residential Units – planning permissions are granted for new
residential units.
2. Commercial Units Converted to Residential Units - planning permissions are granted for the
conversion of existing commercial premises to residential use.
3. Unoccupied, Unfinished or Derelict Properties Reinstated to Residential Use Not Needing Planning
Permission – previously occupied or derelict or unfinished residential buildings are brought back into
use.
4. Planning Permissions Not Proceeded With – some planning permissions will not be proceeded with.
5. Planning Permissions for Student Accommodation – some planning permissions will be for student
accommodation and so will not be for general long-term residential use, either owner-occupied or
rented
6. Reduction in the Number of Rental Units – the number of residential rental units can be reduced.
This specifically refers to changes such as the Housing (Standards for Rented Houses) Regulations
20083 where so-called pre-63 rental units which were exempt from certain planning regulations were
brought into the scope. These conditions came into operation on 1 February 2013. This impact of
this was the number of rental units that could be accommodated in a property was reduced.
7. Once Off Units Not For Sale – once-off residential units, while adding to the overall residential
dwelling stock, are generally, though not exclusively, built by individuals who are moving from
existing accommodation.
8. Existing Residential Units Lost – some of the existing residential stock will be lost through fire,
dereliction, demolition to, for example, create space for new residential building.
3 S.I. No. 534/2008 http://www.irishstatutebook.ie/eli/2008/si/534/made/en/print
5. How Many Net New Residential Units Are Really Available?
Page 5
9. Rental Units Converted to Residential – this happens when, for example, some rental units (such as
those that were classified as pre-63 – see above) would have been sold as the owner did not deem the
cost of achieving compliance with the new regulations was worth the investment.
10. New Residential Units Sold To Institutional Investors – newly-constructed residential units will be
sold to institutional investors for rental rather than being available for purchase by owner-occupiers.
11. Net New Stock of Residential Units Sold To Owners/ Occupiers – this is the net new residential stock
available for sale.
The following chart shows the information from a number of sources. However, it is a starting point for a
more detailed analysis. The information is shown for the interval 2011Q1 to 2018Q1. The information is
only shown at a national level. Some of the data is available for longer intervals and/or at monthly
frequencies and at a more granular geographical level. However this represents the local common
denominator for all the data.
The data show is:
Planning Permissions Units – planning permissions granted for residential units4. This is important
because it represents a leading indicator of new residential properties that will be available in the
future. The lag between planning permission and the associated properties being constructed and for
sale is very variable.
ESB Connections – connections of residential property, either newly built property or previously
disconnected (a property is generally de-energised after 6 months of not being occupied) residential
reconnected5
New House Guarantee Registrations – These represent registrations HomeBond for warranties on new
homes that are meant to cover major structural defects over a ten year period. These registrations
are normally issued one month before work commences on the site.
Commencement All Residential Dwellings - A Commencement Notice is notified to a Building Control
Authority (a Local Authority) that a person intends to carry out either works. These relate to notices
for residential dwellings.6
New Residential Property Purchases – this is information taken from the residential property price
register7. There are many issues with the quality of this data. In the context of this analysis, the
greatest issue is that sales of large numbers of residential units are recorded as single transactions.
New Dwellings Completed – this is taken from the new CSO data series8
4 BHQ05: Planning Permissions Granted for New Houses and Apartments by Type of Dwelling, Quarter and
Statistic https://www.cso.ie/px/pxeirestat/Statire/SelectVarVal/Define.asp?maintable=BHQ05&PLanguage=0
5 http://www.housing.gov.ie/housing/statistics/house-building-and-private-rented/construction-activity-esb-
connections
6 HSM13: Commencement Notices by Local Authority, Residential Units Commenced and Month
https://www.cso.ie/px/pxeirestat/Statire/SelectVarVal/Define.asp?maintable=HSM13&PLanguage=0
7 https://www.propertypriceregister.ie/website/npsra/pprweb.nsf/page/ppr-home-en
8 NDQ01: New Dwelling Completions by Type of House and Quarter
https://www.cso.ie/px/pxeirestat/Statire/SelectVarVal/Define.asp?maintable=NDQ01&PLanguage=0
6. How Many Net New Residential Units Are Really Available?
Page 6
The data in the chart is:
Quarter Planning
Permissions
Units
ESB
Connections
New House
Guarantee
Registrations
Commencement
All Residential
Dwellings
New
Residential
Property
Purchases
New Dwellings
Completed
2011Q1 3,667 2,766 274 1,402 752 1,875
2011Q2 3,310 2,561 168 1,235 751 1,791
2011Q3 2,512 2,590 183 1,014 684 1,687
2011Q4 2,156 2,563 209 714 762 1,641
2012Q1 1,355 1,931 144 806 553 1,131
2012Q2 1,406 1,998 195 1,317 623 1,117
2012Q3 1,638 2,107 125 960 754 1,205
2012Q4 1,851 2,452 163 959 1,234 1,458
2013Q1 2,308 1,691 219 965 632 889
2013Q2 1,926 2,009 408 1,085 669 1,146
2013Q3 1,409 1,982 417 1,386 897 1,033
2013Q4 1,556 2,619 282 1,272 1,544 1,507
2014Q1 1,604 2,090 489 5,247 852 1,094
7. How Many Net New Residential Units Are Really Available?
Page 7
Quarter Planning
Permissions
Units
ESB
Connections
New House
Guarantee
Registrations
Commencement
All Residential
Dwellings
New
Residential
Property
Purchases
New Dwellings
Completed
2014Q2 1,606 2,742 511 379 1,237 1,318
2014Q3 2,144 2,957 679 923 1,284 1,404
2014Q4 2,057 3,227 895 1,064 2,028 1,702
2015Q1 3,213 2,629 789 1,337 1,161 1,371
2015Q2 3,110 2,996 1,157 2,354 1,935 1,570
2015Q3 2,704 3,289 1,492 2,654 1,416 2,033
2015Q4 4,017 3,752 859 2,402 1,705 2,245
2016Q1 3,091 3,144 1,263 2,902 1,054 1,968
2016Q2 3,141 3,498 1,291 3,323 1,613 2,395
2016Q3 5,814 3,865 1,451 3,015 1,759 2,511
2016Q4 4,329 4,425 1,621 3,994 2,381 3,041
2017Q1 4,650 3,896 2,210 3,860 1,623 2,779
2017Q2 4,453 4,640 2,999 5,408 2,126 3,298
2017Q3 4,739 4,997 2,435 4,061 2,351 3,785
2017Q4 6,934 5,738 1,822 4,243 3,174 4,584
2018Q1 8,405 3,158 2,013 4,374 2,025 3,526
There is a data issue with commencement notices for 2014Q1. There was a switch between an old and a
new method of recording at that time.
All the series show the same trend over the relatively short interval.
At no time during the interval does the number of units for which planning permission was granted come
close to the number of
Data is available for two types of commencement notice: single dwelling (once-off development) and
multiple dwellings (multiple unit development). The following shows the data for the number of units
where the commencement notices were for multiple dwellings and the number of new dwellings recorded
in the new time series at a lag of two quarters.
The number of new dwellings recorded in the new data series closely matches the number of
commencement notices at a lag of two quarters apart from the values for the quarter 2014Q1. So the
number of new dwellings completed is around 89% of the number of commencements 6 months later.
8. How Many Net New Residential Units Are Really Available?
Page 8
The following chart shows the number of dwelling commencements three quarters after the number of
dwelling planning permissions. So about 95% of number of units for which planning permission has been
granted start being constructed 9 months later.
9. How Many Net New Residential Units Are Really Available?
Page 9
Combing the two sets of data gives a very rough estimate or around 85% of the number of dwellings for
which planning permissions are granted translate into new completed dwellings at a lag of 15 months.
Number Of New Dwellings And Census Data
The context for information on the number of new dwellings is the demand for residential
accommodation. This is driven by demographic factors: size of the population and sizes of different
cohorts.
The following chart shows the estimated annual population9 and the size of the housing stock10 for the
years 1996 to 2017. The housing stock numbers are only available for the census years.
9 Taken from PEA11: Population estimates from 1926 by Single Year of Age, Sex and Year
https://www.cso.ie/px/pxeirestat/Statire/SelectVarVal/Define.asp?maintable=PEA11&PLanguage=0
10 Taken from E1071: Housing Stock and Vacancy Rate 1991 to 2016 by County and City, CensusYear and
Statistic https://www.cso.ie/px/pxeirestat/Statire/SelectVarVal/Define.asp?maintable=E1071&PLanguage=0
10. How Many Net New Residential Units Are Really Available?
Page 10
The following table contains the population11 and number of dwellings data and shows the differences
between the years.
Year Estimated
Population
Census
Population
Census
Number of
Dwellings
Census
Population
Difference
Census
Number of
Dwellings
Difference
1991 3,525,719 3,525,719 1,160,249
1996 3,626,087 3,626,087 1,258,948 100,368 98,699
1997 3,664,313
1998 3,703,082
1999 3,741,647
2000 3,789,536
2001 3,847,198
2002 3,917,203 3,917,203 1,460,053 291,116 201,105
2003 3,979,853
2004 4,045,188
2005 4,133,839
2006 4,232,929 4,239,848 1,769,613 322,645 309,560
2007 4,375,842
2008 4,485,070
2009 4,533,395
2010 4,554,763
2011 4,574,888 4,588,252 1,994,845 348,404 225,232
2012 4,593,697
2013 4,614,669
2014 4,645,440
11 The population census data is taken from EY001: Population at Each Census from 1841 to 2016 by County, Sex
and CensusYear
https://www.cso.ie/px/pxeirestat/Statire/SelectVarVal/Define.asp?maintable=EY001&PLanguage=0
11. How Many Net New Residential Units Are Really Available?
Page 11
Year Estimated
Population
Census
Population
Census
Number of
Dwellings
Census
Population
Difference
Census
Number of
Dwellings
Difference
2015 4,687,787
2016 4,739,597 4,761,865 2,003,645 173,613 8,800
2017 4,792,490
The only overlapping interval in the population data, census housing stock data and information on
numbers of new dwellings in from 2011Q1 to 2016Q1. Note that the annual population estimates and the
census population numbers because the measure population at different times of the year,
The following table shows the number of new dwellings12, both newly constructed and unfinished
dwellings (ghost estates) that were completed.
Quarter New Dwellings
Completed
Unfinished Total New
Dwellings
2011Q1 1,875 383 2,258
2011Q2 1,791 331 2,122
2011Q3 1,687 326 2,013
2011Q4 1,641 338 1,979
2012Q1 1,131 278 1,409
2012Q2 1,117 357 1,474
2012Q3 1,205 431 1,636
2012Q4 1,458 385 1,843
2013Q1 889 354 1,243
2013Q2 1,146 323 1,469
2013Q3 1,033 393 1,426
2013Q4 1,507 436 1,943
2014Q1 1,094 422 1,516
2014Q2 1,318 627 1,945
2014Q3 1,404 716 2,120
2014Q4 1,702 603 2,305
2015Q1 1,371 342 1,713
2015Q2 1,570 453 2,023
2015Q3 2,033 384 2,417
2015Q4 2,245 492 2,737
2016Q1 1,968 409 2,377
Total 31,185 8,783 39,968
So the number of new dwellings between the 2011 and 2016 census was 39,968 but the difference in the
housing stock recorded at these two censuses was just 8,800. So, assuming these sets of data are correct,
the number of new dwellings was offset by a reduction of 31,168 dwellings elsewhere.
12 The data is taken from NDQ04: ESB Connections by Type of Connection and Quarter
https://www.cso.ie/px/pxeirestat/Statire/SelectVarVal/Define.asp?maintable=NDQ04&PLanguage=0
12. How Many Net New Residential Units Are Really Available?
Page 12
One likely cause of this reduction was the introduction of the pre-63 regulations referred to on page 4.
There will be other factors that will cause a reduction in housing stock. However, other census recorded
large increases in housing stock. The pre-63 regulation intervention represented a pro-cyclical regulatory
change whose impact was not evaluated before it was implemented.
Based on the assumption that around 85% of the number of planned units become actual units in 15
months (see page 9), the current stock of planned residential units will translate roughly into just under
25,000 new units by mid-2019. This is a very small increase. This does not take into account any loss of
residential housing stock during the same interval.
Summary
Counting the number of new dwellings while important needs to be conducted in a wider context where
factors that affect the reduction in the number of dwellings and demographic changes that affect
demand for dwellings.
Focussing on the narrow issue of new dwellings may be a distraction on the wider problem of an
increasing population and thus a greater demand for residential accommodation and changes that cause
a reduction in dwellings.
There was virtually no net increase in the number of dwellings between the 2011 and 2016 census. There
was a net increase of just 8,800 new dwellings while the population increased by 348,404.