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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.

By observing facts of the “reversal of agglomeration” of Chinese enterprises during the period of rapid Internet development and using a new economic geography model combined with the data of the real estate sector, this paper deduces the influence of the “reshaping mechanisms” of the Internet on China's economic geography based on the “gravitation mechanism” of the Internet that affects the enterprises and the “amplification mechanism” of the Internet that amplifies the dispersion force of house prices.

In the empirical aspect, the dynamic spatial panel data model is used to test the micromechanisms of the impact of the Internet on enterprises' choice of location and the instrumental variable method is used to verify the macro effects of the Internet in reshaping economic geography.

It is found that in the era of the network economy, the Internet has become a source of regional competitive advantage and is extremely attractive to enterprises. The rapidly rising house price has greatly increased the congestion cost and has become the force behind the dispersion of enterprises. China's infrastructure miracle has closed the access gap which gives full play to network externalities and promotes the movement of enterprises from areas with high house prices to areas with low house prices.

The Internet is amplifying the dispersion force of congestion costs manifested as house prices and is reshaping China's economic geography. This paper further proposes policy suggestions such as taking the Internet economy as the new momentum of China's economic development and implementing the strategy of regional coordinated development.

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.

The purpose of this study is to identify the situation of spatial inequality in the residential system of Tehran city in terms of housing prices in the year 2021 and to examine its changes over time (1991–2021).

In terms of purpose, this study is applied research and has used a descriptive-analytical method. The statistical population of this research is the residential units in Tehran city 2021. The average per square meter of a residential unit in the level of city neighborhoods was entered in the geographical information system (GIS) in 2021. Moran’s spatial autocorrelation method, map cluster analysis (hot and cold spots) and Kriging interpolation have been used for spatial analysis of points. Then, the change in spatial inequality in the residential system of Tehran city has been studied and measured based on the price per square meter of a residential unit for 30 years in the 22 districts of Tehran by using statistical clustering based on distance with standard deviation.

The result of spatial autocorrelation analysis with a score of 0.873872 and a p-value equal to 0.000000 indicates a cluster distribution of housing prices throughout the city. The results of hot spots show that the highest concentration of hot spots (the highest price) is in the northern part of the city, and the highest concentration of cold spots (the lowest price) is in the southern part of Tehran city. Calculating the area and estimating the quantitative values of data-free points by the use of the Kriging interpolation method indicates that 9.95% of Tehran’s area has a price of less than US$800, 17.68% of it has a price of US$800 to US$1,200, 25.40% has the price of US$1,200 to US$1,600, 17.61% has the price of US$1,600 to US$2,000, 9.54% has the price of US$2,000 to US$2,200, 6.69% has the price of US$2,200 to US$2,600, 5.38% has the price of US$2,600 to US$2,800, 4.59% has the price of US$2,800 to US$3,200 and finally, the 3.16% has a price more than US$3,200. The highest price concentration (above US$3,200) is in five neighborhoods (Zafaranieh, Mahmoudieh, Tajrish, Bagh-Ferdows and Hesar Bou-Ali). The findings from the study of changes in housing prices in the period (1991–2021) indicate that the southern part of Tehran has grown slightly compared to the average range, and the western part of Tehran, which includes the 21st and 22nd regions with much more growth than the average price.

There is massive inequality in housing prices in different areas and neighborhoods of Tehran city in 2021. In the period under study, spatial inequality in the residential system of Tehran intensified. The considerable increase in housing prices in the housing market of Tehran has made this sector a commodity, intensifying the inequality between owners and non-owners. This increase in housing price inequality has caused an increase in the informal living for the population of the southern part. This population is experiencing a living situation that contrasts with the urban plans and policies.

This paper aims to examine the housing market responses to two outbreaks of respiratory diseases in Hong Kong during the Information Era – the 2003 SARS and COVID-19 outbreaks.

The authors first investigate the aggregate housing price changes during SARS and COVID-19. Next, the authors conduct a battery of univariate analyses pertaining to the relationship between district-level housing price movements and geographic and demographic patterns during the pandemic periods. Finally, to shed light on the housing price dynamics at the micro level, the authors conduct an estate-level analysis with the data of 234 residential estates from 2003 to 2020, focusing on the impacts of SARS and COVID-19 on the idiosyncratic volatility of residential estates.

Overall, SARS and COVID-19 outbreaks are negatively associated with housing prices. However, unlike SARS, the impact of COVID-19 on housing prices was moderate and transient. The geographic imbalances of the epidemic-induced underperformance are observed at the district and estate levels. Finally, the estate-level analysis presented in this paper indicates that the average idiosyncratic volatility of residential estates is 1.5% higher during the SARS period but 3.7% lower during the COVID-19 period. Lower volatility during COVID-19 is likely explained by household learning from the SARS period.

Regulators and investors could resort to efficient information disclosure to attenuate idiosyncratic volatility's adverse impact on housing market returns.

To the best of the authors’ knowledge, the authors are among the first to examine housing market responses to the 2003 SARS and COVID-19 outbreaks using the Hong Kong housing market as a laboratory.

This paper aims to examine the convergence pattern of residential house prices in a panel of 55 major cities in Turkey over the period between 2010 and 2018 and to investigate the determinants of convergence club formations.

The authors applied the log t-test to identify the convergence clubs and estimated ordered logit model to determine the key drivers.

The results suggest that there are five convergence clubs and confirm the heterogeneity of the Turkish housing market. Istanbul, the commercial capital, and Mugla, an attractive tourist destination, are at the top of the housing market and followed by the cities located in the western part, particularly along the Aegean and Mediterranean coasts of Turkey. Moreover, the ordered logit model results point out that the differences in employment rate, climate, population density and having a metropolitan municipality play a significant role in determining convergence club membership.

Large-scale policy measures aiming to increase employment opportunities in rural cities of central and eastern provinces and providing lower land prices and property taxes in the metropolitan cities of Turkey can help mitigate some of the divergence in the house prices across cities.

The novelty of this study lies in employing a new data set at the city level containing 55 cities in Turkey, which is by far the largest in terms of city coverage among emerging market economies to implement the log t-test. It also contributes to the literature on city-specific determinants of convergence club formation in the case of an emerging economy.

The purpose of this paper is to analyse the interdependencies of the house price growth rates in Australian capital cities.

A vector autoregression model and variance decomposition are introduced to estimate and interpret the interdependences among the growth rates of regional house prices in Australia.

The results suggest the eight capital cities can be divided into three groups: Sydney and Melbourne; Canberra, Adelaide and Brisbane; and Hobart, Perth and Darwin.

Based on the structural vector autoregression model, this research develops an innovative interdependence analysis approach of regional house prices based on a variance decomposition method.

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.

The purpose of this study is to reveal the dynamics of house prices and sales in spatial and temporal dimensions across British regions.

This paper incorporates two empirical approaches to describe the behaviour of property prices across British regions. The models are applied to two different data sets. The first empirical approach is to apply the price diffusion model proposed by Holly et al. (2011) to the UK house price index data set. The second empirical approach is to apply a bivariate global vector autoregression model without a time trend to house prices and transaction volumes retrieved from the nationwide building society.

Identifying shocks to London house prices in the GVAR model, based on the generalized impulse response functions framework, I find some heterogeneity in responses to house price changes; for example, South East England responds stronger than the remaining provincial regions. The main pattern detected in responses and characteristic for each region is the fairly rapid fading of the shock. The spatial-temporal diffusion model demonstrates the presence of a ripple effect: a shock emanating from London is dispersed contemporaneously and spatially to other regions, affecting prices in nondominant regions with a delay.

The main contribution of this work is the betterment in understanding how house price changes move across regions and time within a UK context.

This study aims to investigate the spatial distribution of housing prices and identify the affecting factors (independent variable) on the cost of residential units (dependent variable).

The method of the present study is descriptive-analytical and has an applied purpose. The used statistical population in this study is the residential units’ price in Tehran in 2021. For this purpose, the average per square meter of residential units in the city neighborhoods was entered in the geographical information system. Two techniques of ordinary least squares regression and geographically weighted regression have been used to analyze housing prices and modeling. Then, the results of the ordinary least squares regression and geographically weighted regression models were compared by using the housing price interpolation map predicted in each model and the accurate housing price interpolation map.

Based on the results, the ordinary least squares regression model has poorly modeled housing prices in the study area. The results of the geographically weighted regression model show that the variables (access rate to sports fields, distance from gas station and water station) have a direct and significant effect. Still, the variable (distance from fault) has a non-significant impact on increasing housing prices at a city level. In addition, to identify the affecting variables of housing prices, the results confirm the desirability of the geographically weighted regression technique in terms of accuracy compared to the ordinary least squares regression technique in explaining housing prices. The results of this study indicate that the housing prices in Tehran are affected by the access level to urban services and facilities.

Identifying factors affecting housing prices helps create sustainable housing in Tehran. Building sustainable housing represents spending less energy during the construction process together with the utilization phase, which ultimately provides housing at an acceptable price for all income deciles. In housing construction, the more you consider the sustainable housing principles, the more sustainable housing you provide and you take a step toward sustainable development. Therefore, sustainable housing is an important planning factor for local authorities and developers. As a result, it is necessary to institutionalize an integrated vision based on the concepts of sustainable development in the field of housing in the Tehran metropolis.