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Understandings of house prices and their interrelationships have undoubtedly drawn a great amount of attention from various market participants. This study aims to investigate the monthly newly-built residential house price indices of seventy Chinese cities during a 10-year period spanning January 2011–December 2020 for understandings of issues related to their interdependence and synchronizations.

Analysis here is facilitated through network analysis together with topological and hierarchical characterizations of price comovements.

This study determines eight sectoral groups of cities whose house price indices are directly connected and the price synchronization within each group is higher than that at the national level, although each shows rather idiosyncratic patterns. Degrees of house price comovements are generally lower starting from 2018 at the national level and for the eight sectoral groups. Similarly, this study finds that the synchronization intensity associated with the house price index of each city generally switches to a lower level starting from early 2019.

Results here should be of use to policy design and analysis aiming at housing market evaluations and monitoring.

This study aims to investigate dynamic relationships among residential housing price indices of ten major Chinese cities for the years 2005–2021.

Using monthly data, this study uses vector error correction modeling and the directed acyclic graph for characterization of contemporaneous causality among the ten indices.

The PC algorithm identifies the causal pattern and the Linear Non-Gaussian Acyclic Model algorithm further determines the causal path, from which this study conducts innovation accounting analysis. Sophisticated price dynamics are found in price adjustment processes following price shocks, which are generally dominated by the top tiers of cities.

This study suggests that policies on residential housing prices in the long run might need to be planned with particular attention paid to these top tiers of cities.

The purpose of this paper is to provide technically robust indicators of housing market performance from the records held by the Ghana Land Valuation Board, through the construction of the first ever residential price and rent indices for the aggregate and disaggregate markets.

The approach involved time series produced from hedonic models using 3,250 transaction‐based data, running from 1992 to 2007, and documents on movements in capital and rental values in Accra and Tema, the dominant commercial conurbations in the country.

The paper makes a major contribution to knowledge and understanding of housing market dynamics in Ghana. The results suggest that the derived price and rent indices look, at first sight, reasonably plausible with cyclical trends showing weak and strong patches.

The paper focuses on the development of formal housing markets through a detailed case study of Ghana, and provides findings and models of a wider application in other emerging economies.

The purpose of this study is to contribute to the relatively narrow existing residential real estate literature by developing and validating several univariate forecasting models, to reliably anticipate future house price dynamics across several European Union (EU) countries.

The research approach relies on the time series analysis, by using the Box–Jenkins autoregressive integrated moving average (ARIMA) methodology to explore the trends of residential property prices in selected EU countries and to obtain a snapshot of the potential signs of change to be witnessed by domestic residential markets on a short time-period. The analysis has been performed distinctly for each country in the sample, to account for country-specific past and future trends as well as similarities in their house price growth rate evolutions. The models were estimated for a broad sample of quarterly observations during 1990-2015, while the forecast horizon ranged between the third quarter of 2015 and the fourth quarter of 2016.

The findings suggested that residential property prices’ real growth rate can be modeled through the Box–Jenkins method for France, The Netherlands, Sweden and UK. The pattern of Italy’s residential property prices’ real growth rate cannot be explained by means of univariate ARIMA models, being more suited for multivariate models.

The article subscribes to the need for timely, high-frequency and quality data about house price trends in Europe, to increase the accuracy of forecasts and prevent the appearance of bubbles on real estate market. It compares residential property prices’ dynamics across European countries to identify housing markets with similar patterns of their prices.

This paper examines commonly used property price indices in several Commonwealth countries. It finds that many of the measures may be flawed owing to two issues relating to the index construction methodology: the quality change problem and the choice of an index number algorithm. Using data that comprises the universe of transactions for the Singapore residential market, alternative indices based on more rigourous estimation models are constructed that aim to mitigate these problems. When compared to the official benchmark indices, deviations in time series price behaviour are evident particularly for short‐run dynamics. A key implication of the results is the importance of explicitly recognizing the biases that can arise from using extant indices. Otherwise, a reliance on flawed index signals for decision‐making may result in distorted allocations.

The Chinese housing market has gone through rapid growth during the past decade, and house price forecasting has evolved to be a significant issue that draws enormous attention from investors, policy makers and researchers. This study investigates neural networks for composite property price index forecasting from ten major Chinese cities for the period of July 2005–April 2021.

The goal is to build simple and accurate neural network models that contribute to pure technical forecasts of composite property prices. To facilitate the analysis, the authors consider different model settings across algorithms, delays, hidden neurons and data spitting ratios.

The authors arrive at a pretty simple neural network with six delays and three hidden neurons, which generates rather stable performance of average relative root mean square errors across the ten cities below 1% for the training, validation and testing phases.

Results here could be utilized on a standalone basis or combined with fundamental forecasts to help form perspectives of composite property price trends and conduct policy analysis.

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 purpose of this paper is to compute an aggregate misalignment index using a multiple indicator approach to identify under- or over-valuation of house prices in Malta based on fundamentals.

A total of six indicators are used that capture households, investors and system-wide factors: the house price-to-Retail Price Index ratio, the price-to-hypothetical borrowing volume ratio, price-to-construction costs ratio, price-to-rent ratio, dwelling investment-to-GDP ratio and the loan bearing capacity. The weights are derived using principal component analysis. The analysis is performed using both the house price indices of the National Statistics Office (NSO) and the Central Bank of Malta (CBM), which are based on contract and advertised prices, respectively.

House prices in Malta were overvalued by around 20 to 25 per cent in the pre-crisis boom. This disequilibrium started to be corrected following the decline in house prices, with the CBM and NSO house price cycles reaching a trough in 2013 and 2014, respectively. At the trough, house prices were undervalued by around 10 to 15 per cent. Since then, house prices started to recover although the recovery in advertised prices was more pronounced compared to that based on contract prices. In mid-2017, advertised house prices were slightly overvalued, while contract prices still have to reach their equilibrium level. The dynamics from the misalignment index, including its peaks and troughs, are remarkably similar to the range derived from statistical filters.

Estimates of house price misalignment have both economic and financial stability implications.

This paper allows for a decomposition of the house price cycle, tailored for the particular characteristics of the Maltese housing market. It also takes into account the relationship between house prices and private sector rents, which in recent years have been buoyed, among other factors, by the high inflow of foreign workers and changing patterns in the tourism industry.

The purpose of this paper is to test whether temporal aggregation matters when constructing hedonic house price indices for developing markets using Ghana as a case study.

Monthly, quarterly, semi-yearly and yearly hedonic price indices are constructed and six null hypotheses are tested using the F-ratios to examine the temporal aggregation effect.

The results show that temporal aggregation may not be a serious issue when constructing hedonic house price indices for developing markets as a result of the smaller sample size which these markets normally have. At even 10 per cent significance level, none of the F-ratios estimated is statistically significant. Analysis of the mean returns and volatilities reveal that indices constructed at the lower level of temporal aggregation are very volatile, suggesting that the volume of transactions can affect the level of temporal aggregation, and so, the temporal aggregation level should not be generalised, as is currently observed in the literature.

The diversification importance of real estate and the introduction of real estate derivatives and home equity insurance as financial products call for the construction of robust and accurate real estate indices in all markets. While almost all empirical research recommends real estate price indices to be conducted at the lower level of temporal aggregation, these studies are largely conducted in developed markets where transactions take place frequently and large transaction databases exist. Unfortunately, little is known about the importance of temporal aggregation effect when constructing indices for developing real estate markets. This paper contributes to fill these gaps.

The purpose of this paper is to test the accuracy of the explicit time variable (ETV) and the strictly cross-sectional (SCS) hedonic models when constructing house price indices in developing markets using Ghana as a case study.

The quantitative research methodology is adopted where the accuracy of the two hedonic models used in the construction of house price indices is examined using the mean squared error (MSE) and out-of-sample technique. Yearly indices are constructed for each of the models using 60 per cent of the sample data and 40 per cent is used to forecast house prices for each observations based on which the MSEs are calculated.

The two models produce similar house price trend but the SCS model is more volatile. The ETV model produces the lower MSE, suggesting that it is better to pool data together and includes time dummies (ETV) to estimate indices rather than running separate regressions (SCS) to estimate the index. Using the Morgan–Granger–Newbold test, it is found that indeed the difference between the forecast errors of the two models are statistically significant on a 1 per cent level confirming the accuracy of the ETV model over the SCS model.

This paper has produced convincing results recommending the use of the ETV hedonic model to construct house price indices which is of use to practitioners and academics.

The introduction of financial products like the property derivatives and home equity insurances to the financial market calls for accurate and robust property price indices and the hedonic method is mostly used to construct these indices. While there have been a lot of test conducted as to which variant of the hedonic method to use in developed markets, little is known about the developing markets. This paper contributes to fill these gaps.