Analysis of Housing Starts in the United States
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This document analyzes housing starts in the United States in 2012 to gauge the recovery of the housing sector after the recession. It examines state-level housing start data from the US Census Bureau from 2002-2012, comparing raw numbers and adjusting for population. Preliminary maps show that Texas, Florida, California, New York, North Carolina, Washington, Arizona, Colorado, Virginia and Georgia had the strongest numbers of housing starts in 2012, though some of these states have softer housing markets when adjusted for population. States in the South and Midwest generally show less recovery than those in the South/Southeast and West.
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Sheet1StateRegionPopulationIncomeIlliteracyLife ExpMurderHS GradFr.docx
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2020 Santa Clara County Construction Forecast
The document provides an overview of construction forecasts for 2020, including national, California, and Santa Clara County statistics. Some key points:
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STAT200 Introduction to Statistics Dataset for Written Assig.docx
STAT200 Introduction to Statistics
Dataset for Written Assignments
Description of Dataset:
The data is a random sample from the US Department of Labor’s 2016 Consumer Expenditure Surveys (CE) and provides information about the
composition of households and their annual expenditures (https://www.bls.gov/cex/). It contains information from 30 households, where a survey
responder provided the requested information; it is all self-reported information. This dataset contains four socioeconomic variables (whose names start
with SE) and four expenditure variables (whose names start with USD).
Description of Variables/Data Dictionary:
The following table is a data dictionary that describes the variables and their locations in this dataset (Note: Dataset is on second page of this document):
Variable Name Location in Dataset Variable Description Coding
UniqueID# First Column Unique number used to identify each survey
responder
Each responder has a unique
number from 1-30
SE-MaritalStatus Second Column Marital Status of Head of Household Not Married/Married
SE-Income Third Column Annual Household Income Amount in US Dollars
SE-AgeHeadHousehold Fourth Column Age of the Head of Household Age in Years
SE-FamilySize Fifth Column Total Number of People in Family (Both Adults
and Children)
Number of People in Family
USD-Annual Expenditures Sixth Column Total Amount of Annual Expenditures Amount in US Dollars
USD-Housing Seventh Column Total Amount of Annual Expenditure on Housing Amount in US Dollars
USD-Electricity Eighth Column Total Amount of Annual Expenditure on
Electricity
Amount in US Dollars
USD-Water Ninth Column Total Amount of Annual Expenditure on Water Amount in US Dollars
How to read the data set: Each row contains information from one household. For instance, the first row of the dataset starting on the next page shows
us that: the head of household is not married and is 53 years old, has an annual household income of $97,681, a family size of 4, annual expenditures of
$56,124, and spends $18,676 on housing, $1,468 on electricity, and $551 on water.
https://www.bls.gov/cex/
UniqueID# SE-MaritalStatus SE-Income SE-AgeHeadHousehold SE-FamilySize USD-AnnualExpenditures USD-Housing USD-Electricity USD-Water
1 Not Married 97681 53 4 56124 18676 1468 551
2 Not Married 96727 39 2 56440 18376 1441 542
3 Not Married 95432 51 1 55120 18391 1458 548
4 Not Married 96928 43 3 55932 18701 1479 520
5 Not Married 94929 59 2 55247 18483 1451 546
6 Not Married 95744 52 4 55963 18435 1465 555
7 Not Married 95366 48 2 57082 18576 1478 538
8 Not Married 96697 49 2 56453 18520 1469 545
9 Not Married 96572 59 2 56515 18648 1480 552
10 Not Married 96653 51 4 56488 18838 1470 535
11 Not Married 96664 53 3 55558 18502 1478 553
12 Not Married 96621 54 2 55746 18149 1455 540
13 Not Married 96886 44 2 55321 18312 1450 523
14 Not Married 96244 56 4 56051 18484 1457 539
15 Not Married 94867 60 1 55512 .
Kingston_University_Geodemographic
The document discusses the creation of the 2011 OAC (Office for National Statistics Area Classification), which organized areas into three tiers - Supergroups, Groups, and Subgroups - using statistical techniques. It then provides demographic and geographic data for the area within a 1km buffer of Kingston University, including percentages of the area covered by different 2011 OAC Subgroups and Acorn Types. The data comes from the 2011 UK Census and 2015 Acorn data from CACI, and shows generalizations due to clustering techniques.
100 Metros Update
This document provides a summary of data from the 100 Metros dashboard about the Atlanta metro area and how it compares to other large metro areas in the US. The data is organized into categories including demographics, housing, education/technology, health, employment, economic development, and commuting. Some key findings are that Atlanta's population ranks 8th largest nationally and grew faster than most other large metros from 2021-2022. The metro area also ranks high for housing permits, job growth, and median home sale price increases but lower for median income and average hourly wages.
2016 Construction Forecast
This is our annual construction economic forecast presented to the American Society of Professional Estimators in Santa Clara on March 28, 2016.
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Sheet1StateRegionPopulationIncomeIlliteracyLife ExpMurderHS GradFr.docx
Sheet1StateRegionPopulationIncomeIlliteracyLife ExpMurderHS GradFrostAreaAlabamaSouth361536242.169.0515.141.32050708AlaskaWest36563151.569.3111.366.7152566432ArizonaWest221245301.870.557.858.115113417ArkansasSouth211033781.970.6610.139.96551945CaliforniaWest2119851141.171.7110.362.620156361ColoradoWest254148840.772.066.863.9166103766ConnecticutEast310053481.172.483.1561394862DelawareEast57948090.970.066.254.61031982FloridaSouth827748151.370.6610.752.61154090GeorgiaSouth49314091268.5413.940.66058073HawaiiWest86849631.973.66.261.906425IdahoWest81341190.671.875.359.512682677IllinoisNorth1119751070.970.1410.352.612755748IndianaNorth531344580.770.887.152.912236097IowaNorth286146280.572.562.35914055941KansasWest228046690.672.584.559.911481787KentuckyEast338737121.670.110.638.59539650LouisianaSouth380635452.868.7613.242.21244930MaineEast105836940.770.392.754.716130920MarylandEast412252990.970.228.552.31019891MassachusettsEast581447551.171.833.358.51037826MichiganNorth911147510.970.6311.152.812556817MinnesotaNorth392146750.672.962.357.616079289MississippiSouth234130982.468.0912.5415047296MissouriWest476742540.870.699.348.810868995MontanaWest74643470.670.56559.2155145587NebraskaWest154445080.672.62.959.313976483NevadaWest59051490.569.0311.565.2188109889New HampshireEast81242810.771.233.357.61749027New JerseyEast733352371.170.935.252.51157521New MexicoWest114436012.270.329.755.2120121412New YorkEast1807649031.470.5510.952.78247831North CarolinaSouth544138751.869.2111.138.58048798North DakotaNorth63750870.872.781.450.318669273OhioNorth1073545610.870.827.453.212440975OklahomaWest271539831.171.426.451.68268782OregonWest228446600.672.134.2604496184PennsylvaniaEast118604449170.436.150.212644966Rhode IslandEast93145581.371.92.446.41271049South CarolinaSouth281636352.367.9611.637.86530225South DakotaNorth68141670.572.081.753.317275955TennesseeSouth417338211.770.111141.87041328TexasSouth1223741882.270.912.247.435262134UtahWest120340220.672.94.567.313782096VermontEast47239070.671.645.557.11689267VirginiaEast498147011.470.089.547.88539780WashingtonWest355948640.671.724.363.5326657
Descriptive Statistics Project
• Perform data analysis based on a set of data using Excel 2016
• Group project (2 persons)
• Submission: on Blackboard (upload your copy individually with your
name and partner’s name on the report and filename by Wednesday,
December 12, 2018.
Histogram
• Prepare data table
• Select data range and click Insert>Insert Statistics Chart>Histogram
• Right click at the horizontal axis and select Format Axis
• Choose appropriate bin width or number of bins
• Usually, the number of bins is approximately equal to the square root of the
number of observations. It can be flexible though.
• Be sure to label the chart appropriately. Design>Add Chart Element
Box Plot
• Filter data to see population for the selected regions
• Home>Sort and Filter>Filter
• Select the region
• Select data range and click Insert>Insert Statistics Chart>B.
Leadership Howard08
This document provides demographic data and trends for Howard County, Maryland. It discusses population growth which has averaged around 2,000 new residential units per year. The population is aging with growth among those 65 and older outpacing other groups. Employment has grown by around 25,000 jobs since 2000 and is projected to increase by 70,000 more by 2020. Future development is planned through allocation of housing units across planning regions in the county.
#1The Evans Excel file College Data provides data from different.docx
#1
The Evans Excel file College Data provides data from different colleges and branch campuses within one university over four years.
Calculate descriptive statistics for each of the factors (e.g. Ave ACT, Ave SAT, etc.) for each of the four years of data. Use the Excel Data-Analysis toolpak.
Group the data into Main Campus and Branch Campus. Develop column charts comparing the median for the six factors for the Main Campus and for the Branch Campus.
Develop side by side Box Plots for the grouped data (Main and Branch) for each of the factors, i.e. you should have six side-by-side box plots.
b. Write a one paragraph report to the college’s vice president explaining the information you developed and stating your conclusions.
College DataCollege DataCollegeYearAvg ACTAvg SATHS GPA% top 10%% top 20%1st year retention rateLiberal Arts200721.901041.003.07510.70%19.70%67.65%Liberal Arts200821.911036.443.17013.45%30.57%67.10%Liberal Arts200921.801037.903.17913.70%27.50%68.80%Liberal Arts201022.011061.373.19014.43%29.49%72.00%Music200723.801133.203.28118.20%37.70%76.43%Music200824.031135.673.40117.58%41.76%78.80%Music200924.101148.903.47732.60%53.90%87.70%Music201025.541162.253.49431.53%53.15%86.60%Education 200720.70993.002.9971.80%17.50%70.18%Education 200821.27973.283.18410.96%23.29%67.00%Education 200921.501015.503.19912.70%31.00%75.90%Education 201020.81986.143.0048.40%20.00%67.50%Engineering200726.601200.003.51633.50%62.00%74.17%Engineering200826.471195.523.60342.07%63.79%85.40%Engineering200926.301200.503.56533.70%60.50%82.70%Engineering201026.231206.093.53531.45%56.40%84.90%Business 200722.701064.903.1978.90%19.10%64.33%Business 200822.941075.423.34615.90%33.47%79.10%Business 200922.701065.903.1999.60%22.90%76.90%Business 201022.941077.933.23711.65%24.92%76.70%Architecture200726.101211.503.57436.00%68.40%91.45%Architecture200826.301193.113.60140.46%65.32%89.90%Architecture200926.001194.603.61736.30%67.40%88.50%Architecture201026.251200.573.63733.45%62.72%85.80%North Central Branch Campus200719.00901.002.6232.90%6.69%59.88%North Central Branch Campus200819.06923.812.5421.81%8.46%65.10%North Central Branch Campus200919.00924.002.6031.80%6.00%65.70%North Central Branch Campus201019.22920.232.6713.17%10.32%63.20%Nursing200721.601025.403.27821.10%36.80%72.55%Nursing200821.901010.683.32914.63%21.95%76.40%Nursing200922.701003.403.40220.40%37.00%80.00%Nursing201022.121019.423.34717.58%41.76%73.50%Vocational Technology200721.001014.902.8566.70%16.30%66.30%Vocational Technology200821.061013.242.9055.44%16.33%67.70%Vocational Technology200921.101013.302.8395.90%14.10%73.10%Vocational Technology201018.99936.062.5271.55%5.17%67.00%South Central Branch Campus200719.50932.002.7182.50%15.00%59.88%South Central Branch Campus200819.62897.242.7045.39%14.22%63.60%South Central Branch Campus200919.00924.802.7385.20%14.20%66.90%South Central Branch Campus201019.43961.072.6894.35%13.04%61.80%Health Sciences200721.20956.503.08413.80%34.50%72.55%Health Sciences200 ...
2023 Economic Forecast.pptx
The document provides an economic forecast for the 2023 construction industry. It includes national construction spending forecasts showing increases in both public and private construction from 2021-2022. California construction employment has recovered past pandemic losses. The housing market outlook forecasts a decline in home sales and prices in California in 2023. Local construction starts in the San Jose area are forecast to remain steady in 2023 after increases in 2021-2022. Office and retail vacancy rates in Santa Clara County have declined slightly in 2022.
2007 Growth Summary
This document provides an annual report on demographic and economic trends in Loudoun County, Virginia. Some key highlights include:
- Loudoun County's population grew 3.2% between 2006 and 2007 to over 271,000 people, and is projected to continue rapid growth, increasing 29% over the next decade.
- The number of households in Loudoun County is projected to increase 30% between 2007 and 2017, reaching over 123,000 households.
- The number of housing units surpassed 101,000 in 2007, up 63% from 2000, and is projected to reach over 169,000 units by 2030.
2019 Santa Clara County Construction Forecast
This document provides an economic forecast for construction in the United States, California, and the San Jose/Santa Clara region for 2019. Key points include:
- US construction spending is forecast to increase slightly to $1.309 trillion in 2019, with residential building decreasing 2% and nonresidential matching 2018 levels.
- California's economy is forecast to grow 3.1% in the next 12 months, with housing production leveling off at 130,000 units annually through 2022.
- Construction in San Jose/Santa Clara is forecast to total $5.3 billion in 2018, with 6,360 housing starts expected in 2019, mostly multifamily, to keep pace with population growth.
2017 santa clara county construction forecast
The document provides an overview of construction forecasts for Santa Clara County in 2017. It summarizes national and state construction statistics and trends. For Santa Clara County, it forecasts continued growth in total construction value, with increases in residential, commercial, and institutional building starts. It also notes high construction costs in the county compared to national averages and low unemployment rates in the construction industry locally.
England's North-South Divide on Home Ownership
A modelling approach to establish whether or not there is a north-south divide in the UK in terms of home ownership. Data used included UK Census and UK Quarterly Labour Force Survey
Grade: 78%
The American Community Survey
The American Community Survey (ACS) is the de facto replacement for sample data from the decennial census. The 2010 Census eliminated the long form. Those who want data on income, poverty status, education, the labor force, journey to work, marital status, languages spoken, migration, citizenship, disability, ancestry, military service, or housing characteristics must turn to the American Community Survey.
Descriptive Statistics, Numerical Description
This document discusses descriptive statistics and numerical measures used to describe data sets. It introduces measures of central tendency including the mean, median, and mode. The mean is the average value calculated by summing all values and dividing by the number of values. The median is the middle value when values are arranged in order. The mode is the most frequently occurring value. The document also discusses measures of dispersion like range and standard deviation which describe how spread out the data is. Examples are provided to demonstrate calculating the mean, median and other descriptive statistics.
2020 Santa Clara County Construction Forecast
The document provides an overview of construction forecasts for 2020, including national, California, and Santa Clara County statistics. Some key points:
- National construction spending is forecasted to decrease 4% to $776 billion in 2020, with residential building down 6% and non-residential down 3%.
- In California, housing production reached 120,302 units in 2019, the highest since 2006. The backlog of needed housing units in California is estimated at 2.3 million.
- In Santa Clara County, total construction starts are forecasted to increase slightly to $4.674 billion in 2020, with residential up 9% and non-residential up 7%. Office vacancy rates in the county decreased to 9
STAT200 Introduction to Statistics Dataset for Written Assig.docx
STAT200 Introduction to Statistics
Dataset for Written Assignments
Description of Dataset:
The data is a random sample from the US Department of Labor’s 2016 Consumer Expenditure Surveys (CE) and provides information about the
composition of households and their annual expenditures (https://www.bls.gov/cex/). It contains information from 30 households, where a survey
responder provided the requested information; it is all self-reported information. This dataset contains four socioeconomic variables (whose names start
with SE) and four expenditure variables (whose names start with USD).
Description of Variables/Data Dictionary:
The following table is a data dictionary that describes the variables and their locations in this dataset (Note: Dataset is on second page of this document):
Variable Name Location in Dataset Variable Description Coding
UniqueID# First Column Unique number used to identify each survey
responder
Each responder has a unique
number from 1-30
SE-MaritalStatus Second Column Marital Status of Head of Household Not Married/Married
SE-Income Third Column Annual Household Income Amount in US Dollars
SE-AgeHeadHousehold Fourth Column Age of the Head of Household Age in Years
SE-FamilySize Fifth Column Total Number of People in Family (Both Adults
and Children)
Number of People in Family
USD-Annual Expenditures Sixth Column Total Amount of Annual Expenditures Amount in US Dollars
USD-Housing Seventh Column Total Amount of Annual Expenditure on Housing Amount in US Dollars
USD-Electricity Eighth Column Total Amount of Annual Expenditure on
Electricity
Amount in US Dollars
USD-Water Ninth Column Total Amount of Annual Expenditure on Water Amount in US Dollars
How to read the data set: Each row contains information from one household. For instance, the first row of the dataset starting on the next page shows
us that: the head of household is not married and is 53 years old, has an annual household income of $97,681, a family size of 4, annual expenditures of
$56,124, and spends $18,676 on housing, $1,468 on electricity, and $551 on water.
https://www.bls.gov/cex/
UniqueID# SE-MaritalStatus SE-Income SE-AgeHeadHousehold SE-FamilySize USD-AnnualExpenditures USD-Housing USD-Electricity USD-Water
1 Not Married 97681 53 4 56124 18676 1468 551
2 Not Married 96727 39 2 56440 18376 1441 542
3 Not Married 95432 51 1 55120 18391 1458 548
4 Not Married 96928 43 3 55932 18701 1479 520
5 Not Married 94929 59 2 55247 18483 1451 546
6 Not Married 95744 52 4 55963 18435 1465 555
7 Not Married 95366 48 2 57082 18576 1478 538
8 Not Married 96697 49 2 56453 18520 1469 545
9 Not Married 96572 59 2 56515 18648 1480 552
10 Not Married 96653 51 4 56488 18838 1470 535
11 Not Married 96664 53 3 55558 18502 1478 553
12 Not Married 96621 54 2 55746 18149 1455 540
13 Not Married 96886 44 2 55321 18312 1450 523
14 Not Married 96244 56 4 56051 18484 1457 539
15 Not Married 94867 60 1 55512 .
Kingston_University_Geodemographic
The document discusses the creation of the 2011 OAC (Office for National Statistics Area Classification), which organized areas into three tiers - Supergroups, Groups, and Subgroups - using statistical techniques. It then provides demographic and geographic data for the area within a 1km buffer of Kingston University, including percentages of the area covered by different 2011 OAC Subgroups and Acorn Types. The data comes from the 2011 UK Census and 2015 Acorn data from CACI, and shows generalizations due to clustering techniques.
100 Metros Update
This document provides a summary of data from the 100 Metros dashboard about the Atlanta metro area and how it compares to other large metro areas in the US. The data is organized into categories including demographics, housing, education/technology, health, employment, economic development, and commuting. Some key findings are that Atlanta's population ranks 8th largest nationally and grew faster than most other large metros from 2021-2022. The metro area also ranks high for housing permits, job growth, and median home sale price increases but lower for median income and average hourly wages.
2016 Construction Forecast
This is our annual construction economic forecast presented to the American Society of Professional Estimators in Santa Clara on March 28, 2016.
FPS Demographics Presentation
Presented to the Franklin (MA) School Committee at the meeting of Feb 25, 2020. One piece (albeit a key one) of the puzzle before the school district to resolve. Next piece (facilities capacity analysis) due to be added to the discussion in April, 2020. Plenty of time to digest these numbers in the meantime.
May Market Watch 2021
Residential real estate transactions in the Greater Toronto Area remained high in May 2021 compared to the same period last year, though sales have declined slightly from the peak in March 2021. The average home selling price reached a new record high of $1,108,453 in May 2021, up 28.4% from May 2020. While sales activity has slowed, new listings have also decreased, keeping market conditions tight and upward pressure on home prices. The decline in sales over the past two months is likely due to the absence of typical population growth rather than a cooling housing market.
November 2021 Market Watch
Home sales in the GTA reached a new record high in November 2021, increasing 3.3% over November 2020. However, new listings dropped 13.2% over the same period, highlighting supply issues. The average home price also set a new record, rising 21.7% to $1,163,323. While condo sales increased, prices rose more substantially in suburban areas, showing first-time buyers returning to the market. Ongoing housing affordability challenges will escalate unless all levels of government work to increase housing supply.
Population Estimates, Methods, Procedures & Local Govt
This document discusses population estimates and projections for North Carolina municipalities and counties. It provides population data from 1970 to present and projections out to 2038. It summarizes population change from 2010 to 2018 for North Carolina and other states. It also discusses the roles and responsibilities of the US Census Bureau and North Carolina Office of State Budget and Management in producing population estimates used for planning and distributing state and federal funds. Accurate 2020 Census counts are important for producing reliable population estimates.
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Sheet1StateRegionPopulationIncomeIlliteracyLife ExpMurderHS GradFr.docx
Sheet1StateRegionPopulationIncomeIlliteracyLife ExpMurderHS GradFr.docx
#1The Evans Excel file College Data provides data from different.docx
#1The Evans Excel file College Data provides data from different.docx
STAT200 Introduction to Statistics Dataset for Written Assig.docx
STAT200 Introduction to Statistics Dataset for Written Assig.docx
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Stark Builders: Where Quality Meets Craftsmanship!
At Stark Builders our vision is to redefine the renovation experience by combining both stunning design and high quality construction skills. We believe that by delivering both these key aspects together we are able to achieve incredible results for our clients and ensure every project reflects their vision and enhances their lifestyle.
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Living in an UBER World - June '24 Sales Meeting
June 2024 Lancaster County Sales Meeting for Berkshire Hathaway HomeServices Homesale Realty covering the following topics: 1. VA Suspends Buyer Agent Payment Plan (article), 2. Frequently Used Terms in title, 3. Zillow Showcase Overview, 4. QuickBuy commission promotion, 5. Documenting Cooperative Compensation, 6. NAR's Code of Ethics - Mass Media Solicitations, 7. Is it really cheaper to rent? 8. Do's and Don't's when Terminating the Agreement of Sale, 9. Living in an UBER World
SVN Live 6.17.24 Weekly Property Broadcast
The SVN® organization shares a portion of their new weekly listings via their SVN Live® Weekly Property Broadcast. Visit https://svn.com/svn-live/ if you would like to attend our weekly call, which we open up to the brokerage community.
Anilesh Ahuja Pioneering a Paradigm Shift in Real Estate Success.pptx
Anilesh Ahuja journey is a testament to the power of vision, resilience, and unwavering determination. As a visionary leader, he continues to inspire and empower others to dream big and challenge the status quo. His legacy extends far beyond the realm of real estate, leaving an indelible mark on the industry and the world at large.
Listing Turkey - Piyalepasa Istanbul Catalog
We are working around the clock to transform a long-time dream into reality. As a result, Piyalepasa Istanbul will be the largest privately developed urban regeneration project in Turkey.
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The good old days of the Piyalepasa neighborhood are being brought back to life with Piyalepasa Istanbul houses, residences, offices, hotels and a pedestrianized shopping avenue.
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Analysis of Housing Starts in the United States
- 1. Analysis of Housing Starts in the United States Daniel Kornaus
- 2. The object of this thesis is to gauge the state of recovery in the housing sector (and the larger economy) by analyzing the number of building permits issued in 2012 for housing starts. This data is taken directly from the US Census Bureau. Supporting points: • A comparison of states by the number of housing starts in 2012. • A comparison of trends in individual states over the past decade. • An analysis of housing starts by state, weighted by 2010 Census data
- 3. The methodology: 1. Examine state and national trends in housing starts over the past decade (FY2002 - FY2012.) This will be done using a surface and a series of sparklines. 2. Examine the geographic trends in housing starts using raw data. This is done using a map or surface map. 3. Adjust the raw housing start data vs. 2010 Census population figures. This is done to gauge where the most housing is being constructed per capita. Again, this is depicted using a map or surface map. The data was adjusted using this formula: housing starts / population x1000 to provide an integer for each state to use as a scale for color-coding a map or projecting a surface.
- 4. 2012 2008 2004 0 50000 100000 150000 200000 250000 300000 The leading edge of this figure shows figures for FY2012, and the trailing edge shows figures for FY2002.
- 5. Alabama 18,403 11,385 Alaska Arizona Arkansas California Colorado Conneticut 3,003 995 66,031 21,693 12,436 7,178 159,537 58,540 47,871 23,377 9,731 5,240 Delaware District of Columbia Florida Georgia Hawai’i Idaho Illinois 6,331 4,066 1,591 3,823 185,431 65,039 97,523 23,474 5,902 3,210 13,488 6,753 60,971 13,675 Indiana Iowa Kansas Kentucky Louisiana Maine Maryland Massachusetts Minnesota Mississippi Missouri Montana Nebraska 39,596 13,807 14,798 9,992 12,983 6,047 19,459 8,329 18,425 12,741 7,207 2,788 29,293 14,646 17,465 10,440 Michigan 49,968 11,669 38,977 15,409 11,276 5,000 28,255 11,467 3,574 2,873 9,278 6,535 Nevada New Hampshire New Jersey New Mexico New York North Carolina North Dakota Ohio Oregon Pennsylvania Rhode Island South Carolina South Dakota Oklahoma 35,615 9,034 8,078 2,536 30,441 17,988 12,066 4,871 49,149 23,574 79,824 47,828 3,265 8,086 51,246 17,416 12,979 11,616 22,186 11,222 45,144 19,663 2,848 776 34,104 18,856 4,816 4,432 34,273 19,730 Tennessee 165,027 133,297Texas 19,327 12,520 Utah 3,072 1,258 Vermont 59,445 26,666 Virginia 40,200 28,103Washington 4,890 1,890 West Virginia 38,208 12,082 Wisconsin 2,045 1,877 Wyoming 1,747,651 815,512 United States Note: Years 2006- 2008 are depicted with a red vertical line, to illustrate when the housing sector began to be affected by the recession.
- 6. Preliminary map of findings. TX, FL, CA, NY, NC, WA, AZ, CO, VA, NC, and GA show the strongest numbers of housing starts in 2012. Keep in mind that these are highly populated states.
- 7. Preliminary map of adjusted figures. TX, NC, and CO still show strong housing numbers, while other populated states such as IL, NY, GA, and CA show softer housing markets, when weighted against the number of people living there. The Dakotas show particularly strong growth.
- 8. The conclusion: All states were negatively affected by the housing crash. Some are recovering faster than others. Many states in the south and midwest still show little sign of recovery, while others in the south/southeast and west show stronger numbers in 2012 than in the past 5-6 years. Housing numbers in almost all states were still substan- tially lower in 2012 than they were 8-10 years before. High numbers of housing starts in a particular state are not necessarily indicitive of a productive housing market, as noticed when population is taken into account.