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Determinants of housing prices: evidence from Ontario cities, 2001-2011

Author

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  • Adela Nistor
  • Diana Reianu

Abstract

Purpose - This paper aims to present a panel data econometric model of the main determinants of house prices in the ten largest census metropolitan areas (CMA) in Ontario, Canada, for the years 2001, 2006 and 2011. The impact of immigration on the housing market in Canada is little researched; however, immigration plays an important role into the economy of Canada. According to Statistics Canada, not only is immigration key to Canada’s population growth but also without immigration, in the next 20 years, Canada’s population growth will be zero. The motivation for this study is the bursting of housing bubbles in some developed countries (e.g. USA). The authors analyze variables that are related to the immigration policy in Canada, accounting also for the impact of the interest rate, income, unemployment, household size and housing supply to analyze housing price determinants. The study investigates the magnitude of the impact of the top three leading categories of immigrants to Canada, namely, Chinese, Indian and Filipino, on the housing prices in Ontario’s largest cities. The results show the main factors that explain home prices over time that are interest rate, immigration, unemployment rate, household size and income. Over the 10-year period from 2001 to 2011, immigration grew by 400 per cent in Toronto CMA, the largest receiving area in Ontario, while the nonimmigrant population grew by 14 per cent. For Toronto CMA, immigrants, income, unemployment rate and interest rate explain the CA$158,875 average home price increase over the 2001-2011 time period. Out of this, the three categories of immigrants’ share of total home price increase is 54.57 per cent, with the corresponding interest rate share 58.60 per cent and income share 11.32 per cent of the total price growth. Unemployment rate contributes negatively to the housing price and its share of the total price increase is 24.49 per cent. Design/methodology/approach - The framework for the empirical analysis applies the hedonic pricing model theory to housing sales prices for the ten largest CMAs in Ontario over the years 2001-2011. FollowingAkbari and Aydede (2012) andO’Meara (2015), market clearing in the housing market results in the housing price as a function of several housing attributes. The authors selected the housing attributes based on data availability for the Canadian Census years of 2001, 2006 and 2011 and the variables that have been most used in the literature. The model has the average housing prices as the dependent variable, and the independent variables are: immigrants per dwelling (Chinese, Indian, and Filipino), unemployment rate, average employment income, household size, housing supply and the interest rate. To capture the relative scarcity of dwellings, the independent variable immigrants per dwelling was used. Findings - This study seems to suggest that one cause of high prices in Ontario is large inflows of immigrants together with low mortgage interest rate. The authors focused their attention on Toronto CMA, as it is the main destination of immigrants and comprises the largest cities, including Toronto, Mississauga, Brampton and Oakville. Looking over the 10-year period from 2001 to 2011, the authors can see the factors that impact the home prices in Toronto CMA: immigration, unemployment rate, household size, interest rate and income. Over the period of 10 years from 2001 to 2011, immigrants’ group from China, India and the Philippines account for CA$86,701 increase in the home price (54.57 per cent share of the total increase). Income accounts for CA$17,986 increase in the home price (11.32 per cent share); interest rate accounts for CA$93,103 of the average home price increase in Toronto CMA (58.60 per cent share); and unemployment rate accounts for CA$38,916 decrease in the Toronto average home prices (24.49 per cent share). Household size remain stable over time in Toronto (2.8 average household size) and does not have a contribution to home price change. All these four factors, interest rate, immigrants, unemployment rate and income, together explain CA$158,875 increase in home prices in Toronto CMA between 2001 and 2011. Practical implications - The housing market price analysis may be more complex, and there may be factors impacting the housing prices extending beyond immigration, interest rate, income and household size. Finally, the results of this paper can be extended to include the most recent census data for the year 2016 to reflect more accurately the price situation in the housing market for Ontario cities. Social implications - The fact that currently, in 2017, the young working population cannot afford buying a property in the Toronto CMA area means there is a problem with this market and a corresponding decrease in the quality of life. According toThe Globe and Mail(July 2017), a new pool in 2017 suggested that two in five Canadians believe housing in this country is not affordable for them. Further, 38 per cent of respondents who consider themselves middle or upper class believe in no affordability of housing. The Trudeau Government promised Canadians a national housing strategy for affordable housing. Designing a national housing strategy may be challenging because it has to account for the differential income ranges across regions. Municipal leaders are asking the government to prioritize repair and construct new affordable housing. Another reason discussed in the media of the unaffordability of housing in Toronto and Vancouver is foreign buyers. The Canadian Government recently implemented a tax measure on what it may seem the housing bubble problem: foreign buyers. Following Vancouver, in April 2017, Ontario Government imposed a 15 per cent tax on foreign buyers who are not Canadian citizens or permanent residents. This tax is levied on houses purchased in the area stretching from Niagara Region and Greater Toronto to Peterborough. Originality/value - Few studies use Canadian data to explain house prices and analyze the effect of immigration on housing prices. There is not much research on the effect of the immigrants and immigrants’ ethnicity (e.g., Chinese, Indian and Filipino immigrants), on the housing prices in Canada cities. This study investigates the impact of the most prevalent immigrant races (e.g., from China, India and the Philippines) on housing prices, using data for Canadian major cities in Ontario within a panel data econometric framework. This paper fills this gap and contributes to the literature, which analyzes the determinants of housing prices based on a panel of cities in the Canadian province of Ontario.

Suggested Citation

  • Adela Nistor & Diana Reianu, 2018. "Determinants of housing prices: evidence from Ontario cities, 2001-2011," International Journal of Housing Markets and Analysis, Emerald Group Publishing Limited, vol. 11(3), pages 541-556, May.
  • Handle: RePEc:eme:ijhmap:ijhma-08-2017-0078
    DOI: 10.1108/IJHMA-08-2017-0078
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    Cited by:

    1. Mitra L Devkota & Eric B Howington, 2023. "The Drivers Of Housing Prices And The Impact Of Schools: Evidence From Georgia," Annals - Economy Series, Constantin Brancusi University, Faculty of Economics, vol. 6, pages 6-12, December.

    More about this item

    Keywords

    Housing market analysis; Policy; Economy; Immigrants; Ontario; Regional housing markets; R31; R38; J15; J18;
    All these keywords.

    JEL classification:

    • R31 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Real Estate Markets, Spatial Production Analysis, and Firm Location - - - Housing Supply and Markets
    • R38 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Real Estate Markets, Spatial Production Analysis, and Firm Location - - - Government Policy
    • J15 - Labor and Demographic Economics - - Demographic Economics - - - Economics of Minorities, Races, Indigenous Peoples, and Immigrants; Non-labor Discrimination
    • J18 - Labor and Demographic Economics - - Demographic Economics - - - Public Policy

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