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This paper aims to examine the dynamics of house prices in metropolitan cities in an emerging economy. The purpose of this study is to characterise the house price dynamics and the spatial heterogeneity in the dynamics.

The author explores spatial heterogeneity in house price dynamics, using data for 35 Indian cities with a million-plus population. The research methodology uses panel econometrics allowing for spatial heterogeneity, cross-sectional dependence and non-stationary data. The author tests for spatial differences and analyses the income elasticity of prices, the role of construction costs and lending to the real estate industry by commercial banks.

Long-term fundamentals drive the Indian housing markets, where wealth parameters are stronger than supply-side parameters such as construction costs or availability of financing for housing projects. The long-term elasticity of house prices to aggregate household deposits (wealth proxy) varies considerably across cities. However, the elasticity estimated at 0.39 is low. The highest coefficient is for Ludhiana (1.14), followed by Bhubaneswar (0.78). The short-term dynamics are robust and show spatial heterogeneity. Short-term momentum (lagged housing price changes) has a parameter value of 0.307. The momentum factor is the crucial dynamic in the short term. The second driver, the reversion rate to long-term equilibrium (estimated at −0.18), is higher than rates reported from developed markets.

This research applies to markets that require some home equity contributions from buyers of housing services.

Stakeholders can characterise stable housing markets based on long-term fundamental value and short-run house price dynamics. Because stable housing markets benefit all stakeholders, weak or non-existent mean reversion dynamics may prompt the intervention of policymakers. The role of urban planners, and local and regional governance, is essential to remove the bottlenecks from the demand side or supply side factors that can lead to runaway prices.

Existing literature is concerned about the risk of a housing bubble due to relaxed credit norms. To prevent housing market bubbles, some regulators require higher contributions from home buyers in the form of equity. The dynamics of house prices in markets with higher owner equity requirements vary from high-leverage markets. The influence of wealth effects is examined using novel data sets. This research, documents in an emerging market context, the observations cited in low-leverage developed markets such as Germany and Japan.

This paper aims to construct rental housing indices and identify market segmentation for more effective property-management strategies.

The hedonic model was employed to construct the rental indices. Using the k-means++ and REDCAP (Regionalisation with Dynamically Constrained Agglomerative Clustering and Partitioning) approaches, the authors conducted clustering analysis and identified different market segmentation. The empirical study relied on the database of 80,212 actual rental transactions in Beijing, China, spanning 2016–2018.

Rental housing market segmentation may distribute across administrative boundaries. Properly segmented indices could provide a better account for the heterogeneity and spatial continuity of rental housing and as well be crucial for effective property management.

Residential rent might not only vary over space but also interplays with housing price. It would be worth studying how the rental market functions together with the owner-occupied sector in the future.

Residential rental indices are of great importance for policymakers to be able to evaluate housing policies and for property managers to implement competitive strategies in the rental market. Their constructions largely depend on the analysis of market segmentation, a trade-off between housing spatial heterogeneity and continuity.

This paper fills the gap in knowledge concerning segmented rental indices construction, particularly in China. The spatial constrained clustering approach (REDCAP) was also initially introduced to identify regionalised market segmentation due to its superior performance.

The paper aims to analyse the Shenzhen housing market.

The paper uses an empirical case study using hedonic modelling that includes not only the property specifics, but also the spatial (via {X, Y} coordinates) and household (buyer's) characteristics. Two expansion models are employed to examine the spatial and socio‐economic heterogeneities in housing attribute prices.

The results provide strong evidence that the marginal prices of key housing attributes are not constant but vary with household profile and absolute‐location context within Shenzhen's housing market.

Examining housing market behaviour in major Chinese cities should eventually be approached more explicitly with the development of China's official statistical system.

Largely supported by the valuable transaction data, this paper makes initial and valuable attempts to examine the interaction behaviour between property specifics, location coordinates and buyers’ characteristics within one of the very complex and immature housing markets, namely Shenzhen, China.

This study aims to identify clusters amongst the county housing markets in Poland, taking into account the criteria of size and quality of the housing stock, as well as price level. In addition, this work is intended to detect the socio-economic factors driving the cluster formation.

To group the studied housing markets into homogeneous clusters, this analysis uses a proprietary algorithm based on taxonomic and k-means++ methods. In turn, the generalised ordered logit (gologit) model was used to explore factors influencing the cluster formation.

The results obtained revealed that Polish county housing markets can be classified into three or four homogeneous clusters in terms of the size and quality of the housing stock and price level. Furthermore, the results of the estimation of the gologit models indicated that population density, number of business entities and the level of crime mainly determine the membership of a given housing market in a given cluster.

In contrast to previous studies, this is the first to examine the existence of homogeneous clusters amongst the county housing markets in Poland, taking into account the criteria of size and quality of the housing stock, as well as price level simultaneously. Moreover, this work is the first to identify the driving forces behind the formation of clusters amongst the surveyed housing markets.

Tobler's law of geography states that things that are close to one another tend to be more alike than things that are far apart. In this regard, the spatial pattern of price distribution is defined by the arrangement of individual entities in space and the geographic relationships among them. The purpose of this paper is to provide emerging findings of research analysing the salient factors which impact on the sale price of residential properties using a spatial regression approach.

The research develops and formulates a geographically weighted regression (GWR) model to incorporate residential sales transactions within the Belfast Metropolitan Area over the course of 2010. Transaction data were sourced from the University of Ulster House Price Index survey (2010, Q1‐Q4). The GWR approach was then evaluated relative to a standard hedonic model to determine the spatial heterogeneity of residential property price within the Belfast Metropolitan Area.

This investigation finds that the GWR technique provides increased accuracy in predicting marginal price estimates, in comparison with traditional hedonic modelling, within the Belfast housing market.

This study is one of only a few investigations of spatial house price variation applying the GWR methodology within the confines of a UK housing market. In this respect it enhances applied based knowledge and understanding of geographically weighted regression.

The purpose of this paper is to address the heterogeneity of real estate assets with regard to investment risk measurement, with Paris’ apartment market as a case study.

Quantile regression is used to handle the fact that willingness to pay for housing attributes may vary greatly over both space and asset value categories. The method is alternately applied on central and peripheral districts of Paris, or “arrondissements”, with hedonic indices built for nine deciles over a 17-year period (1990-2006). Portfolio allocation is subsequently analysed with deciles being the assets.

The findings suggest that during the slump, peripheral districts show better resilience and define the efficient frontier while also exhibiting a lower volatility. In addition, higher returns are observed for lower-priced apartments, both central and peripheral. During the recovery and boom stages of the cycle, the highest returns are experienced for the cheapest apartments in central locations, whereas upper-priced, centrally located units yield the lowest returns.

The originality of this research resides in the application of quantile regression in a real estate investment and risk management context. The methodology may raise individual investors’ and practitioners’ attention, especially index providers’.

This paper aims to use Markov switching vector auto regression (MSVAR) methods to examine UK house price cycles in UK regions at NUTS1 level. There is extensive literature on UK regional house price dynamics, yet empirical work focusing on the duration and magnitude of regional housing cycles has received little attention. The research findings indicate that the regional structure of UK exhibits that UK house price changes are best described as two large groups of regions with marked differences in the amplitude and duration of the cyclical regimes between the two groups.

MSVAR principal component analysis NUTS1 data are used.

The housing cycles can be divided into two super regions based on magnitude, duration and the way they behave during recession, boom and sluggish periods. A north-south divide, a uniform housing policy and a monetary policy increase the diversion among the regions.

Markov switching needs high-frequency data and long time spans.

Questions a uniform housing policy in a heterogeneous housing market. Questions the impact of monetary policy on a heterogeneous housing market. The way the recovery of the housing market varies among regions depends on regional economic performance, housing market structure and the labour market. House price convergence, beta-convergence.

No such work has been done looking at duration and magnitude of regional housing cycles. A new econometric method was used.

This paper aims to examine the dynamic impacts of the COVID-19 pandemic and government policy on real house price indices in five emerging economies, namely, Brazil, China, Thailand, Turkey and South Africa.

The authors use the local projection method with a panel data set of these countries spanning from January 2020 to July 2021.

The number of COVID-19 confirmed positive cases raised housing prices, whereas government containment measures reduced them. Both conventional and unconventional monetary policy implemented by central banks to cope with the COVID-19 helped increase housing prices. These effects were strengthened by the US monetary policy via globalized financial markets.

First, while previous researches typically concentrated on developed countries, the authors investigate emerging economies where proportionally more people were badly affected by the pandemic. Second, a panel data set of five emerging economies enabled the authors to examine the dynamic effects of the COVID-19 crisis on housing prices. Third, to the best of the authors’ knowledge, this is the first study evaluating the influences of easing monetary policy on housing prices in emerging economies during the pandemic.

This chapter develops a framework for quantile regression in binary longitudinal data settings. A novel Markov chain Monte Carlo (MCMC) method is designed to fit the model and its computational efficiency is demonstrated in a simulation study. The proposed approach is flexible in that it can account for common and individual-specific parameters, as well as multivariate heterogeneity associated with several covariates. The methodology is applied to study female labor force participation and home ownership in the United States. The results offer new insights at the various quantiles, which are of interest to policymakers and researchers alike.