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The purpose of this paper is to examine the housing price volatility for eight capital cities in Australia over 1987‐2007. Specifically, the volatility of Australian housing and its determinants were investigated.

An exponential‐generalised autoregressive conditional heteoskedasticity (EGARCH) model was employed to analyse the volatility for eight capital cities in Australia. The Engle LM test was also utilised to examine the volatility clustering effects in these cities.

The volatility clustering effects (ARCH effects) were found in many Australian capital cities. The importance of estimating each individual city's EGARCH model was also demonstrated in which the determinants of housing volatility vary from a city to another city. Asymmetric of the positive and negative shocks were also documented.

This study has implications for investors and policy makers in which housing investors should estimate the conditional variance (EGARCH process) of a housing market in respect to the volatility of housing series is not always constant over time. Furthermore, policy makers should also address the importance of considering the sub‐national factors in formulating the national housing policy. The analysis and results are limited by the quality of the data.

This paper is one of the few studies in housing volatility. Additionally, it is probably the first attempt to assess the volatility spillover effects in the Australian housing market.

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.

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. Following Akbari and Aydede (2012) and O’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.

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.

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.

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 to The 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.

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.

In this chapter, we aim to analyze the housing market development in Czechia, in particular the development of housing prices over the last 25 years. We quantify and discuss three distinct periods of excessive growth of regional Czech housing prices, identified through the formation of large positive GAPs – (1) before the entrance of Czechia to the European Union (EU), (2) at the onset of the Global Financial Crisis GFC, (3) in 2021. In all these periods, we identify significant differences among regions. We find that GAPs above 15% may be considered an indication of unsustainable long-term housing price growth that will be followed by a correction.

We then employ fixed effect panel data model to determine the drivers of flat and house prices in 14 Czech regions. Our results show that wage growth, migration and crime rate are significant factors affecting the prices of both flats and houses. Nevertheless, the impact of GDP per capita and job market indicators differs between flats and houses. Moreover, we find that higher migration into the region increases the difference between the prices of houses and flats, while increasing GDP per capita growth and crime rate mitigate this difference significantly.

Housing sufficiency is an indication of national development, and in recognition of this, a longstanding development objective of the Malaysian Government is the provision of affordable housing. The government has introduced various policies, schemes and regulations to increase housing supply. However, despite these measures, homeownership rates are dropping, and housing prices are outstripping inflation. For this reason, this paper aims to explain the determinants of housing choices. The issues in affordable housing supply in Malaysia are that of shortage and distributions. The problem of distribution is largely addressable through choice reconciliations.

The research is based on a cross-sectional survey questionnaire, comprising 20 determinants and 468 householders/users. The questionnaire was developed via a review of the literature and the authors’ experience. The survey forms are administered by hand.

Six determinants were found to be extremely important to households’ choice of housing. The Kaiser’s measure of sampling adequacy (MSA) indicated that the strength of the relationships among the determinants was strong (MSA = 0.762). Bartlett’s test of sphericity, was significant χ² (1035) = 5013.814, p < 0.001), indicating the data were drawn from the same population and that the determinants were related. Using principal component analysis, all the 20 determinants were reduced to seven factors that accounted for some 60 per cent of the total proportion communalities. The factors were general factor, financial factor, building factor, income factor, accessibility factor, market factor and location factor.

Previous research only addressed factors affecting housing price, not a choice. This is the first study that explains determinants of housing choice determinants in Malaysia. This is the first study that involves large respondents. Previous research addressed housing in general and not affordable housing. The results will be useful to developers, homebuyers and policy makers towards affordable housing delivery.

This paper studies the dynamics and elasticities of house prices in Spain and Portugal (Iberia) at the Metropolitan Statistical Area (MSA) level, addressing panel regression problems such as heterogeneity and cross-sectional dependence between MSA.

The authors develop a two steps study. First, five distinct estimation methodologies are applied to estimate the long-term house price equilibrium of the Iberian MSA house market: Mean Group (MG), Fully Modified Ordinary Least Square (FMOLS) MG (FMOLS-MG), FMOLS Augmented MG (FMOLS-AMG), Common Correlated Effects MG (CCEMG) and Dynamic CCEMG (DCCEMG). FMOLS-AMG is found to be the best estimator for the long-term model. Second, an additional five distinct estimation methodologies are applied to estimate the short-term house price dynamics using the long-term FMOLS-AMG estimated price in the error-correction term of the short-term dynamic house price model: OLS Fixed Effects (FE), OLS Random Effects (RE), MG, CCEMG and DCCEMG. DCCEMG is found to be the best estimator for the short-term model.

The results show that in the long run Iberian house prices are inelastic to aggregate income (0.227). This is a much lower elasticity than what was previously found in US MSA house price studies, suggesting that there are other factors explaining Iberian house prices. According to our study, coastal MSA presents an inelastic housing supply and a price to income elasticity close to one, whereas inland MSA are shown to have an elastic supply and a non-significant price to income elasticity. Spatial differences are important and cross-section dependence is prevalent, affecting estimates in conventional methodologies that do not account for these limitations, such as OLS-FE and OLS-RE. Momentum and mean reversion are the main determinants of short-term dynamics.

Recent econometric advances that account for slope heterogeneity and cross-section dependence produce more accurate estimates than conventional panel estimation methodologies. The results suggest that house markets should be analyzed at the metropolitan level, not at the national level and that there are significant differences between short-term and long-term house price determinants.

To the best of the authors' knowledge, this is the first study applying recent econometric advances to the Iberian MSA house market.

With this paper, the authors aim to investigate the drivers behind three of the most important aspects of the Chinese real estate market, housing prices, housing rent and new construction. At the same time, the authors perform a comprehensive empirical test of the popular 4-quadrant model by Wheaton and DiPasquale.

In this paper, the authors utilize panel cointegration estimation methods and data from 35 Chinese metropolitan areas.

The results indicate that the 4-quadrant model is well suited to explain the determinants of housing prices. However, the same is not true regarding housing rent and new construction suggesting a more complex theoretical framework may be required for a well-rounded explanation of real estate markets.

It is the first time that panel data are used to estimate rent and new construction for China. Also, it is the first time a comprehensive test of the Wheaton and DiPasquale 4-quadrant model is performed using data from China.

Residential price levels in Spain vary broadly among markets. Real estate theory explains that prices depend on market characteristics such as vacancy level, land availability, construction supply elasticity to respond to high or low speed to changes on the demand, as well as potential for economic growth, industrial and services activities located inside urban areas, etc. An analysis of prices in Spanish main cities shows that tensions appear to exist in some of them where economic activity shows different dynamism and price level appears to be independent of it. This paper tries to find evidence of the existing relationship between residential prices and economic and demographic factors that are demand determinants such as wages, migrations and productive structure, among others, to explain price formation in Spanish cities. It uses panel data and GLS methodology applied to 71 main Spanish province capitals and cities with more than 100,000 inhabitants. The results show evidence of determinants of housing prices and how some relationships appear to exist between price levels and families’ waged income as well as with population and productive structure in Spanish cities.

This paper is motivated by a concern about the ability of the average Malaysian income to catch up with the rapidly increasing house prices in Peninsular Malaysia. Financial innovation in financial system now regards houses as a financial asset and speculation vehicle. Therefore, a house purchase is made to acquire not merely a necessity but also a financial asset which can generate future returns. Given the problems in the housing market, this paper aims to examine the determinants of house prices in Malaysia, including those such as income, population, foreign inflow and speculation.

This study adopts panel data analyses, namely, the fixed effect model (FEM) and the pooled mean group (PMG), and uses data at state level in quarterly frequency, spanning from 2005Q1 to 2013Q4.

Based on the results of FEM, these determinants influence house prices significantly. Moreover, the PMG results suggest that there is convergence in the model, which are indicated by the significant and negative sign of the error correction term. In conclusion, the rapidly increasing house price is not caused by speculation activities in the housing market. More precisely, Malaysian income is capable of catching up with the increasing house prices.

As income remains to be one of the major drivers in influencing Malaysian house price, Malaysian Government shall continue the policies of supply low cost houses to the low-income groups and My First Home Scheme (SRP) by offering less stringent rules in applying house loan for the first-time house buyers.

This study used the actual data of foreign housing purchase obtained from Malaysia Valuation and Property Services Department to represent foreign inflow; therefore, the results will reflect the impact of foreign inflow in a better manner.

This paper aims to assess the long-run drivers and short-term dynamics of real house prices in Sweden for 1986Q1 to 2016Q4. More specifically, the author examines the extent to which real house prices are determined by affordability, demographics and asset price factors.

The author conducts a cointegration analysis and applies a vector autoregression model to examine the long- and short-run responsiveness of Swedish real house prices to a number of key categories of fundamental variables.

The empirical results indicate that house prices will increase in the long run by 1.04 per cent in response to a 1 per cent increase in household real disposable income, whereas real after-tax mortgage interest and real effective exchange rates show average long-term effects of approximately – 8 and – 0.7 per cent, respectively. In addition, the results show that the growth of real house prices is affected by growth in mortgage credit, real after-tax mortgage interest rates and disposable incomes in the short run, whereas the real effective exchange rate is the most significant determinant of Swedish real house appreciation.

The impact of the two lending restrictions been implemented after the financial crisis – the mortgage cap in October 2010 and the amortization requirement in June 2016 – are ineffective to stabilize the housing market. This suggests that macroprudential measures designed to ease pressure on housing prices and reduce risks to financial stability need to focus on these fundamentals and address the issues of tax deductibility on mortgage rates and the gradual implementation of debt-to-income limits to contain mortgage demand and improve households’ resilience to shocks.

The purpose of this paper is to analyze empirically major factors that affect housing prices in Istanbul, Turkey using the classification and regression tree (CART) approach.

The data set was collected from various internet pages of real estate agencies during June 2007. The CART approach was then applied to derive main results and to make implications with regard to the housing market in Istanbul, Turkey.

The CART results indicate that sizes, elavators, existance of security, existance of central heating units and existance of view are the most important variables crucially affecting housing prices in Istanbul. The average price of houses in Istanbul was found to be 373,372.36 New Turkish Liras. The average size of a house was 138.37 m². The average age of houses is 15.07 years old with the average number of rooms being 3.11. The average number of baths is 1.43 and average number of toilets is 1.22. Only 5 percent of homes have storage space, 45 percent of homes have parking space, 64 percent of homes are heated with furnace, whereas only 29 percent of homes are used central heating system. Among the 31 variables employed in this study, it was concluded size, elavator, security, central heating unit and view are the most important factors that have impact on housing prices in housing market in Istanbul.

Future research and analysis of housing market in Istanbul and in Turkey can benefit from the method used in this study and findings derived from this research to come up with more general model(s) to include more houses in a wide range of regions in Turkey to analyze the determinants of housing prices in Turkey in general.

Examining housing prices using the CART model is relatively new in the field of housing economics. Additionally, this study is the first to use the CART model to analyze housing market in Istanbul and in Turkey and derive valuable housing policies to be used by the authorities.