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Implications of Population Aging on Real Housing Prices
 

Implications of Population Aging on Real Housing Prices

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This study projects the impact of population aging on future housing stock and prices in both provincial and national markets. ...

This study projects the impact of population aging on future housing stock and prices in both provincial and national markets.

Mario Fortin,
Professor,
Université de Sherbrooke

Statistics

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  • Real price peaked in BC in 1994. After slowly receding for 7 years, is exploded after 2001 to reach the top value of 400 000$ in 2007. Alberta’s prices steadily declined after 1981 and took a serious upward momentum only in 2005 to pass beyond the Canadian average in 2006. Manitoba and Saskatchewan have since lowest and most stable prices. In 2007, prices in Saskatchewan have been growing very fastly. The Canadian average mimics the price behaviour of Ontario: peaks in 1988, virtually stable real prices until 2000, then a steady growth to reach the recent peak of almost 280 000$.
  • - To proxy the demand/supply imbalance caused by aging, I calculated the ratio between population size in the age group 25-44 (prime age) and population size in the 65+ age group. In all regions the ratio was almost stable in the 80s but has started to decline between 1986 (Quebec) and 1990. - New Foundland had the youngest population in the 80s but is becoming the oldest place : the ratio falls rapidly. In Quebec, the ratio if falling almost as rapidly. It is now Ontario that has the youngest population in eastern and central Canada.
  • Alberta has by far the most favourable demographic ratio of all Canadian provinces. The oldest province is Saskatchewan.
  • To adapt the formula to housing it is necessary to add, uncluding depreciation f such that the formula becomes (1+  )/(i +  + f -  )
  • Point A is the initial long-run equilibrium. Following an increase in housing demand, caused by (for example) a rise in Y or a fall in r, the price jumps to B in the short run. High price increases the profit in home building and the supply starts moving to the right. The final equilibrium is at point C where price equals cost. There is a final short run vertical supply curve (not shown on the graph) that intersects the final demand curve a point C.
  • Not surprisingly, the lowest addition to housing stock is in New Foundland and the faster is in Ontario.
  • Addition to the housing stock has been much faster in western provinces, particularly in Alberta and British Columbia. Annual variations in BC were extremely wide between 1990 and 1998. Starting in 1998 and for 5 years, the annual growth was lower in BC than the Canadian average, but construction has since picked up. At the opposite, Manitoba and particularly Saskatchewan has had very low construction.
  • The lowest price are observed in the Atlantic provinces and New Foundland and the highest is in Ontario Real price peaked in 1988 in Ontario and Quebec. It picked up again significantly only after 2000 in both provinces and have now exceeded the previous peak.
  • Real price peaked in BC in 1994. After slowly receding for 7 years, is exploded after 2001 to reach the top value of 400 000$ in 2007. Alberta’s prices steadily declined after 1981 and took a serious upward momentum only in 2005 to pass beyond the Canadian average in 2006. Manitoba and Saskatchewan have since lowest and most stable prices. In 2007, prices in Saskatchewan have been growing very fastly. The Canadian average mimics the price behaviour of Ontario: peaks in 1988, virtually stable real prices until 2000, then a steady growth to reach the recent peak of almost 280 000$.
  • Alberta has by far the most favourable demographic ratio of all Canadian provinces. The oldest province is Saskatchewan.
  • Alberta has by far the most favourable demographic ratio of all Canadian provinces. The oldest province is Saskatchewan.
  • In the price equation : Change in real income has a positive impact (7%) while change in the interest rate a strong and highly significant impact on housing price : a one percentage point rise decreases housing price by 4.7%. Lag price is not significant but lagged real income, interest rate and stock are significant. In the stock equation : a 1% increase in real price increases by 0.0245% the change in the stock while the lagged stock has a coefficient of -0.045. In the Canadian system, population change has a positive but not significant impact on price.
  • In the price equation : Change in real income has a positive impact (7%) while change in the interest rate a strong and highly significant impact on housing price : a one percentage point rise decreases housing price by 4.7%. Lag price is not significant but lagged real income, interest rate and stock are significant. In the stock equation : a 1% increase in real price increases by 0.0245% the change in the stock while the lagged stock has a coefficient of -0.045. In the Canadian system, population change has a positive but not significant impact on price.
  • In the price equation : Change in real income has a positive impact (7%) while change in the interest rate a strong and highly significant impact on housing price : a one percentage point rise decreases housing price by 4.7%. Lag price is not significant but lagged real income, interest rate and stock are significant. In the stock equation : a 1% increase in real price increases by 0.0245% the change in the stock while the lagged stock has a coefficient of -0.045. In the Canadian system, population change has a positive but not significant impact on price.
  • In the price equation : Change in real income has a positive impact (7%) while change in the interest rate a strong and highly significant impact on housing price : a one percentage point rise decreases housing price by 4.7%. Lag price is not significant but lagged real income, interest rate and stock are significant. In the stock equation : a 1% increase in real price increases by 0.0245% the change in the stock while the lagged stock has a coefficient of -0.045. In the Canadian system, population change has a positive but not significant impact on price.
  • In the price equation : Change in real income has a positive impact (7%) while change in the interest rate a strong and highly significant impact on housing price : a one percentage point rise decreases housing price by 4.7%. Lag price is not significant but lagged real income, interest rate and stock are significant. In the stock equation : a 1% increase in real price increases by 0.0245% the change in the stock while the lagged stock has a coefficient of -0.045. In the Canadian system, population change has a positive but not significant impact on price.
  • In the price equation : Change in real income has a positive impact (7%) while change in the interest rate a strong and highly significant impact on housing price : a one percentage point rise decreases housing price by 4.7%. Lag price is not significant but lagged real income, interest rate and stock are significant. In the stock equation : a 1% increase in real price increases by 0.0245% the change in the stock while the lagged stock has a coefficient of -0.045. In the Canadian system, population change has a positive but not significant impact on price.
  • Alberta has by far the most favourable demographic ratio of all Canadian provinces. The oldest province is Saskatchewan.
  • Alberta has by far the most favourable demographic ratio of all Canadian provinces. The oldest province is Saskatchewan.
  • Alberta has by far the most favourable demographic ratio of all Canadian provinces. The oldest province is Saskatchewan.

Implications of Population Aging on Real Housing Prices Implications of Population Aging on Real Housing Prices Presentation Transcript

  • The impact of population aging on the provincial housing markets in Canada Presentation to the CMHC Mario Fortin, Ph. D. Professor Département d’économique April 1 st , 2009
  • Background
    • In 1989, Mankiw and Weil (MW) projected an imminent decline in US real housing price caused by the aging of the baby boomers. This would happen because they estimated that housing consumption steadily declines after the age of 40.
    • In the following years, many studies concluded differently : housing consumption does not decline after 40 but remains almost flat until around 70.
    • Moreover, interest rates and real per-capita income are more important than demography in determining housing prices, as the post 2000 housing bubble showed.
    • Yet, 20 years later, population is still aging and the question of an eventual negative impact on housing price remains pertinent.
  • Goal of the project
    • This projects aims to investigate the impact of population aging on the Canadian and provincial housing markets.
    • An international literature survey showed that changes in disposable income and interest rates (nominal or real) are always identified as the most important determinants of housing price movements.
    • Demography is often, but not systematically, found to have an impact. When mentioned, it is measured in various ways, such as migration flows, population growth, ratio of generation size, etc.
    • Other variables such as household wealth are sometimes found significant.
  • Aging in Canadian provinces
    • Population growth varies considerably between provinces.
    • New Foundland, the Maritimes and Saskatchewan have experienced recently either a declining or a stable population. At the other extremes, Alberta, BC in the 90s and, until recently, Ontario have had population growth faster than the national average.
    • The falls in natality in the 60s has caused population aging but because of inter-provincial migration, its rate has occurred at a different rate in the provinces.
    • In the next 25 years, the dispersion of the provincial median age will continue to increase: aging is projected to affect more the eastern part of Canada.
  • Ratio of 25-44 to 65+: eastern and central Canada
  • Ratio of 25-44 to 65+: Canada and western provinces
  • The financial approach to housing price
    • The financial approach to housing price focuses on the price-to-rent ratio , a concept similar to the price earnings ratio used in stock market valuation models.
    • The right valuation of a house is the present value (PV) of expected future earnings discounted at the risk-adjusted discount rate.
    • This approach is very hard to use because of 3 unknown values: 1. the growth rates of future rents and housing prices; 2. the risk premium required to hold housing capital; 3. the current rent provided by owner-occupied houses.
    • Because of these obstacles, this approach is not possible in Canada.
  • The structural approach to housing price
    • Housing demand depends on the determinants seen previously : real per-capita income, demography (size and/or age) and a measure of user cost.
    • Housing price is in the short term the result of the interaction between housing demand and the supply of housing inherited from the previous period.
    • For profit home builders accelerate (reduce) construction when housing price is higher (lower) than cost. Therefore, housing price gravitates around the the long-run minimum cost.
    • Because short-run and long-run supply curves differ, the dynamic plays an essential part in the estimation.
  • Housing price dynamics
  • Required data
    • The structural model is estimated on the period 1980-2007 while the forecast period is 2008-2031.
    • Housing price is measured by the MLS average transaction price. (Source : CREA)
    • Interest rate, population, CPI and personal disposable income are extracted from CANSIM.
    • Data on the number of housing units (Stock of housing) is extracted from CANSIM until 2000. For the period 2001 to 2007, the number of units has been calculated in a manner consistent with previous estimates made by Statistics Canada.
    • Rather than population, demography is based on the evolution on the number of households.
  • Change in stock : Eastern and central Canada
  • Change in stock : Canada and western provinces
  • Real housing price : eastern and central Canada
  • Real housing price : Canada and western provinces
    • Since no annual estimation of the number of households are published, these estimations have been built from census data.
    • The age-specific headship rate is the ratio between the number of households headed by a person of a given age and the population at this age group.
    • The aggregate headship rate is the reverse of the household’s average size.
    • In the last 25 years, we observed a reduction in the headship rate for all people below 45, with a particularly fast drop for people below 30. At the opposite, there has been a steady increase in the headship rate for people over 50, trends that have slowed down recently.
    Evolution of the age-specific headship rate
  • The age-specific provincial headship rates
    • The highest headship rate below 30 is observed in Saskatchewan and Quebec but Quebecers between 35 and 70 have the highest headship rates. This may be due to a higher proportion of non-family households.
    • At the opposite, the lowest age-specific headship rates are observed in New Foundland, Ontario and BC.
    • The headship rate of senior citizens over 75 drops in Quebec and New Foundland while it continues to rise with age in all other provinces.
    • Because the headship rate is increasing with age, an aging population will display a trend increase in the headship rate even if the age-specific headship rate is stable.
    • Aging explains most of the positive trend in the overall headship rate observed in Canada but partly also the change in the age-specific headship rate over time.
    • Because Eastern Canada is aging faster the trend in the headship rate is stronger than in the west. New Foundland has the fastest growing headship rate.
    • Since 1991, Quebec has the highest overall headship rate. But although Quebec is aging faster than the rest of Canada, we also observe that Quebec has the highest age-specific headship rate.
    Historical changes in the headship rate
  • Steps to estimate the number of households
    • 1. To calculate the age-specific headship rate for each province a linear interpolation between census was applied. Ex. If the age-specific headship is 20% in 1981 and 21% in 1986, it increases annually by 0,2% between these two census.
    • 2. The population in a given age group in a given year is multiplied by the age-specific headship rate to obtain the number of households in each age group which are added up to get the aggregate number of households.
    • 3. To project the number of households after 2006, the average annual change between 1986 and 2006 (a period of twenty years) in the age-specific headship rate is pursued linearly in all subsequent years.
  • The number of households exceeds the number of housing units
    • In 2007, the number of households exceeded the stock of housing by 14% (5% in 81-82) and the excess rate varied widely between provinces. A CMCH research highlight (Socio-economic 08-004) explains the time trend essentially by the popularity of secondary house for baby boomers.
    • New Foundland and Saskatchewan have the largest excess rate (around 20%) and Alberta the lowest (9%).
    • In provinces that ages more, a slower population growth allows for lower housing prices. But at the same time, there is a higher ratio of housing stock to the number of households.
  • Excess of housing units over the number of households
  • Excess of housing units over the number of households
  • Broad description of the model
    • The model consists has 2 equations estimated simultaneously for 8 provincial models, that is, 16 equations.
    • The model is built is such a way that long run adjustment in stock and price are different than short run.
    • The model allows for a dynamic retroaction between price and stock.
    • The lagged ratio housing stock/number of households has a negative impact on price. On the other hand, current price has a positive impact on the change in housing stock.
    • The results confirm the crucial role of nominal interest rate and real income in changing housing price.
  • Provincial coefficients
    • With few exceptions, the housing market works in similar ways in all provinces.
    • The main exception is LOG(STO(-1))/HOU(-1)) which varies in the price equation and D(LOG(HOU)) which is twice higher in the construction equation in BC, and almost zero in the Atlantic regions.
    • Stock reacts to housing price. The change in stock thereafter introduces a gradual response of price because of ratio of housing stock to the number of households changes.
  • Impact of demography
    • Demography appears at two places.
    • Current change in the number of households influences the change in housing stock. A 1% increase in the growth in the number of households induces a 0.293% increase in the housing stock the same year. This impact is 0.527 in BC but only 0.029 in Atlantic.
    • Demography appears also in the fact that the lagged ratio between the number of housing units and the number of households has a negative impact on housing price.
  • Population and households forecasts
    • StatCan publishes 13 population scenarios along three main assumptions : low, medium and high growth. Each case differs on the combination of trends in interprovincial or international migrations and low or high fecondity.
    • For simulation purpose I have retained the scenarios 1 (low growth), 3 (medium case in fecondity and migration) and 6 (high growth).
    • Because the actual population is known until 2007 while population forecast starts in 2006, I have multiplied each projection by the ratio of the 2007 population to 2007 projection. This adjustment has been made for each age group (15-19, 20-24, etc...) and in each province.
    • Population in each province and age group has been transformed into a number of households by multiplying by the corresponding age-specific headship rate.
  • Other economic hypothesis
    • The annual growth rate in real per-household income was supposed to be the same as the average between 1981 and 2007, that is, 0,509% per year.
    • As to the nominal interest rate, it was set to its value for the 5-year fixed rate mortgage rate 7.07%.
    • Only these base case are presented today. However, the projections are constructed so that the user can modify the hypothesis of the projections.
  • Projected growth in the number of households
  • Number of households and housing units
  • Projection of the Canadian average housing price
  • Projections of provincial housing price (scenario 2)
  • Conclusion
    • Aging will not be a significant drag on housing price before 2020. Even then, real housing price is likely to plateau in the second half of the 20s when the number of households will stop growing. The real housing price should then vary between $380 000 and $450 000 in 2027, depending on the scenario.
    • The model forecast that a growing gap will emerge between housing stock and the number of households in the 20s, mirroring the path observed in Atlantic provinces in the last twenty years.
    • Because of this, housing stock should continue to grow even when the number of households will reach its maximum.
  • Conclusion (continued)
    • At the regional level, British Columbia and Alberta will still be the most expensive places to live, with a stronger growth in Alberta which could be the province with the highest average price.
    • Ontario will still have an average price higher than the Canadian average while all other provinces are foreseen to maintain a lower than average housing price.
    • Quebec, Manitoba and Saskatchewan will have similar path for price while the Atlantic should continue to be the places with the lowest price.