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This paper aims to analyze the median price for existing single-family housing units in Las Cruces, New Mexico. The proposed theoretical model accounts for the interplay between supply and demand sides of a metropolitan housing market.

This study analyzes the median price for existing single-family housing units in Las Cruces, New Mexico. The proposed theoretical model accounts for the interplay between supply and demand sides of a metropolitan housing market. Explanatory variables used in the analysis are real per capita income, the housing stock, real mortgage rates, real apartment rents and the median real price of single-family units in the USA. Annual frequency data are collected for a 1971–2017 sample period. Parameter estimation is completed using two-stage generalized least squares. Empirical results confirm several, but not all, of the hypotheses associated with the underlying analytical model. In particular, Las Cruces housing prices are found to be reliably correlated with local income and national housing prices.

Empirical results confirm several of the hypotheses associated with the underlying analytical model. In particular, Las Cruces housing prices are found to be reliably correlated with local income and national housing prices.

Results obtained support only a subset of the hypothetical relationships associated with the theoretical model. Additional testing for other small and/or medium sized is required to clarify whether these outcomes are unique to Las Cruces.

Local income fluctuations and national housing price fluctuations appear to be reliably related to housing price fluctuations for this metropolitan economy.

Comparatively little housing market research has been conducted for small and medium size urban economies. There is no guarantee that results obtained for large metropolitan housing markets are representative of smaller regional housing markets. The model developed has fairly moderate data requirements and may be applicable to other small and medium size economies such as Las Cruces.

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.

The purpose of this paper is to introduce an empirical model for house price spillovers between real estate markets. The model is presented by using data from the US-UK and London-New York housing markets over a period of 1975Q1-2016Q1 by employing both static and dynamic methodologies.

The research analyzes long-run static and dynamic spillover elasticity coefficients by employing three methods, namely, autoregressive distributed lag, the fully modified ordinary least square and dynamic ordinary least squares estimator under a Kalman filter approach. The empirical method also investigates dynamic correlation between the house prices by employing the dynamic control correlation method.

The paper shows how a dynamic spillover pricing analysis can be applied between real estate markets. On the empirical side, the results show that country-level causality in housing prices is running from the USA to UK, whereas city-level causality is running from London to New York. The model outcomes suggest that real estate portfolios involving US and UK assets require a dynamic risk management approach.

One of the findings is that the dynamic conditional correlation between the US and the UK housing prices is broken during the crisis period. The paper does not discuss the reasons for that break, which requires further empirical tests by applying Markov switching regime shifts. The timing of the causality between the house prices is not empirically tested. It can be examined empirically by applying methods such as wavelets.

The authors observed a unidirectional causality from London to New York house prices, which is opposite to the aggregate country-level causality direction. This supports London’s specific power in the real estate markets. London has a leading role in the global urban economies residential housing markets and the behavior of its housing prices has a statistically significant causality impact on the house prices of New York City.

The house price co-integration observed in this research at both country and city levels should be interpreted as a continuity of real estate and financial integration in practice.

The paper is the first research which applies a dynamic spillover analysis to examine the causality between housing prices in real estate markets. It also provides a long-term empirical evidence for a dynamic causal relationship for the global housing markets.

The purpose of this paper is on developing and implementing a model which provides a fuller and more comprehensive reflection of the interaction of house prices at the suburb level.

The authors examine how changes in housing prices evolve across space within the suburban context. In doing so, the authors developed a model which allows for suburbs to be connected both because of their geographic proximity but also by non-spatial factors, such as similarities in socioeconomic or demographic characteristics. This approach is applied to modelling home price dynamics in Melbourne, Australia, from 2007 to 2018.

The authors found that including both spatial and non-spatial linkages between suburbs provides a better representation of the data. It also provides new insights into the way spatial shocks are transmitted around the city and how suburban housing markets are clustered.

The authors have generalized the widely used SAR model and advocated building a spatial weights matrix that allows for both geographic and socioeconomic linkages between suburbs within the HOSAR framework. As the authors outlined, such a model can be easily estimated using maximum likelihood. The benefits of such a model are that it yields an improved fit to the data and more accurate spatial spill-over estimates.

The purpose of this study, the nonlinear relationship between the real estate market and the stock market was investigated in Iran. For this intent, the monthly data from 2012:4 to 2022:5 is used.

In this study, the quantile-on-quantile estimation method is used, which is a combination of the nonparametric estimation methods and the quantile regression.

The research results show that, in the low quantiles, the effect of stock market return on the housing market return is negative or zero. In fact, in this situation, the increasing returns in the stock market will shift part of the financial resources of the economy to the market and create stagnation or even negative returns in the housing market. This situation is seen more strongly in some other quantiles, including the 0.25 and 0.75 quantiles; in contrast, the effect of high quantiles of stock market returns is positive on the housing market.

It seems that the demand in the housing market increase in a situation where the returns of the stock market are growing, and the market is in a bullish condition, and this causes an increase in the price and returns in this market. In addition, the results show that the effect of stock market returns on capital market returns is asymmetric and nonlinear.

This paper aims to explain the main empirical facts of housing markets, notably the trade-off between housing price and time-on-the-market, the positive correlation between housing price and the number of contracts traded during a given period (i.e. the trading volume) and the existence of price dispersion.

This theoretical paper makes use of a search and matching model. Search and matching, indeed, are two fundamental characteristics of the trading process in the housing market, and, thus, the search-and-matching models have become the new economic approach to the analysis of real estate markets.

This paper shows that a slightly modified version of the baseline search and matching model à la Mortensen-Pissarides can explain the main empirical facts of housing markets. There are two key mechanisms that allow to achieve this notable goal: a simple formalisation of the (reasonable) assumption that buyers today are potential sellers tomorrow (and vice versa); and the direct relationship between market tightness and house price, derived by the standard matching model and underestimated by the related literature.

The developed theoretical model only studies the equilibrium conditions. Indeed, it would be interesting to also study the disequilibrium in housing markets.

The explanation of the main empirical facts of housing markets is embodied in the same and relatively simple theoretical model.

In addition to the explanation of the main empirical facts of housing markets, the developed theoretical model can generate an upward sloping Beveridge curve in the housing market (the positive relation between home-seekers and vacant houses). Instead, according to a recent criticism in the related literature, a model à la Mortensen-Pissarides inherently generates a (empirically unrealistic) downward sloping Beveridge curve.

This paper aims to study the relationship between the rental and selling prices, a very important topic that forms the fundamentals of real estate markets.

This theoretical paper makes use of a search and matching model of the housing market. The search and matching models are the benchmark models of the “matching” markets, such as the labour market and the housing market, where trade is a decentralised, uncoordinated and time-consuming economic activity.

Unlike the previous related literature, where this relation is usually analysed in the context of the present value equation, this paper shows the existence of a “dual” relation between rental and selling prices as follows: one in the homeownership market and another one in the rental market. This “dual” relation connects the rental and homeownership markets and allows to get equilibrium in both markets with positive house prices.

Several topics could be deepened for making the paper richer and more interesting, although at the cost of much more mathematics. First of all, the introduction of specific functional forms for both the rent function and the sale price function, so as to calculate both the elasticity of rent with respect to sale price and the elasticity of sale price with respect to rent. In this way, it would be possible to understand how each market (rental and homeownership) reacts to shock and policies that affect the other market.

In general, this framework could help policymakers to design housing policy reforms that take into consideration the effects on both markets. Indeed, some policies could have positive effects on rental markets but perverse effects on homeownership markets and vice versa.

None of the existing and related works of research have considered how to take advantage of the search and matching approach to derive both a “rent function” and a “sale price function” that connect closely the rental and homeownership markets.

This paper aims to study the phenomenon known as “house price dispersion”, one of the most important distinctive features of housing markets. House price dispersion refers to the phenomenon of selling two houses with very similar attributes and in near locations at the same time but at very different prices.

This theoretical paper makes use of a search and matching model of the housing market. The search and matching models are the benchmark models of the “matching” markets, such as the labour market and the housing market, where trade is a decentralised, uncoordinated and time-consuming economic activity.

Unlike the previous related literature that attributes to the heterogeneity of buyers and sellers a significant part of the price volatility, in this paper, the house price dispersion depends on the housing tenure status of home-seekers in the house search process. Indeed, in the presence of different housing tenure status of home-seekers, the house search process leads to different types of matching. In turn, this implies different surpluses (the sum of the net gains of the parties involved in the trade), and eventually, different surpluses produce different prices of equilibrium.

An interesting research agenda for future works would be an extension of the model to study the effect of “online housing search” on the house search and matching process, and thus, on the house price dispersion.

The main practical implication of this work is that the house price dispersion is an inherent phenomenon in the house search and matching process.

None of the existing and related works of research have considered how to take advantage of the search and matching approach to deal with the phenomenon known as “house price dispersion”, without relying on the ex ante heterogeneity of the parties but looking at the “core” of the house search and matching process.

This paper aims to assess the equilibrium house price in the city of Suva (Fiji) and to analyse the house price bubble in the Fiji housing market.

This paper adopts a time series approach to determine the presence of house price bubbles in Fiji over the period from 1988 to 2018.

The findings suggest that real income, land cost, building material price, inflation rate, volatility, household size and wealth have a positive impact on house prices, whereas user cost of capital and political disturbances have a negative impact. The findings further indicate that the Fijis’ housing market does not constitute any house price bubble.

This paper draws policy implications for a small developing state (Fiji) and other similar economies.

The price bubble in the Fiji housing market is analysed for the first time. This paper develops a comprehensive empirical approach to assess the equilibrium-housing price in Fiji.

This paper aims to assess the effects of housing market shocks on real output in South Africa, by focusing on the real private consumption channel.

It measures housing market shocks as non-monetary housing shocks, uses a data set covering the period 1969Q4-2014Q4 and uses the agnostic identification procedure.

The paper finds that 20 per cent of the variation in house prices is explained by these shocks. The paper also finds that the effects of housing demand shocks on real private consumption are short-lived and generate a transitory real output response. Overall, housing demand shocks have managed to explain nearly 13 per cent and 14 per cent of the variation in real private consumption and real output respectively, over 20-quarters ahead forecast revision.

This finding suggests that shocks emanating from the housing market in the country are essential and should be considered when making macroeconomic policy decisions.

None of the existing studies, to our knowledge, have empirically assessed the effects of housing market shocks on real output directly. This paper attempts to contribute to the literature by assessing the direct impact of housing market shocks on the real output, using South Africa as a case study.