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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 examine the effect of proximity and spatial dependence on the house price index for the nascent market Dar es Salaam, Tanzania. Despite the ongoing housing market transactions, there is no single house price index that takes into account proximity and spatial dependence. The proximity considerations in question are proximal to arterial roads, public hospitals, an airport and food markets. Previous studies on sub-Saharan Africa have focused on the ordinary least squares (OLS)-based hedonic model for the index and ignored spatial and proximity considerations.

Using the OLS and spatial econometric approach, the paper tests for the significance of the two effects – proximity and spatial dependence in the hedonic price model with year dummy variables from 2010 to 2019. The paper then compares the three indices in the following configurations: without the two effects, with proximity factors only, and with both effects, i.e. proximity and spatial dependence.

The inclusion of proximity factors and spatial dependence – spatial autocorrelation – seems to improve the hedonic price model but does not significantly improve the house price index. However, further research should be called for on account of the nascent nature of the market.

The paper brings new knowledge by demonstrating that it may not be necessary to take into account proximity factors and spatial dependence for the Dar es Salaam house price index.

The purpose of this study is to develop a spatio-temporal neighbourhood-level house price index (STNL-HPI) incorporating a geographic information system (GIS) functionality that can be used to improve the house price indexation system.

By using the Malaysian house price index (MHPI) and application of geographically weighted regression (GWR), GIS-based analysis of STNL-HPI through an application called LHPI Viewer v.1.0.0, the stand-alone GIS-statistical application for STNL-HPI was successfully developed in this study.

The overall results have shown that the modelling and GIS application were able to help users understand the visual variation of house prices across a particular neighbourhood.

This research was only able to acquire data from the federal government over the period 1999 to 2006 because of budget limitations. Data purchase was extremely costly. Because of financial constraints, data with lower levels of accuracy have been obtained from other sources. As a consequence, a major portion of data was mismatched because of the absence of a common parcel identifier, which also affected the comparison of this system to other comparable systems.

Neighbourhood-level HPI is needed for a better understanding of the local housing market.

This paper aims to study to what extent an insurance based on a house price index provides equity protection for homeowners.

The paper uses a novel dataset of all housing market transactions in the metropolitan area of Melbourne 1990‐2006, to construct repeated sales indices of various temporal spatial aggregation. These indices are used to discuss the efficiency of index‐based insurance schemes. The paper also considers efficiency under different specifications of legitimate claims.

It is found that the payout efficiency is surprisingly stable (around 50 percent) for all temporal spatial aggregations. A neighborhood index outperforms the metropolitan index with respect to target efficiency (the probability of payout given a loss). The introduction of maturity times, say legitimate claim five years after purchase, does improve efficiency somewhat. However, the idiosyncratic component of housing market transactions remains high, and the insurance probably unattractive from a homeowner perspective.

To the authors' knowledge, this is the first time an index‐based insurance scheme is analyzed using real‐market transactions.

The purpose of this paper is to create an endurance index of housing investor sentiment and use it to forecast housing stock returns. This study performs not only in-sample and out-of-sample forecasting, like many previous studies did, but also a true forecasting by using all lag terms of independent variables. In addition, an evaluation procedure is applied to quantify the quality of forecasts.

Using a binomial probability distribution model, this paper creates an endurance index of housing investor sentiment. The index reflects the probability of the high or low stock price being the close price for the Philadelphia Stock Exchange Housing Sector Index. This housing investor sentiment endurance index directly uses housing stock price differentials to measure housing investor reactions to all relevant news. Empirical results in this study suggest that the index can not only play a significant role in explaining variations in housing stock returns but also have decent forecasting ability.

Results of this study reveal the considerable forecasting ability of the index. Monthly forecasts of housing stock returns have an overall accuracy of 51 per cent, while the overall accuracy of 8-quarter rolling forecasts even reaches 84 per cent. In addition, the index has decent forecasting ability on changes in housing prices as suggested by the strong evidence of one-direction causal relations running from the endurance index to housing prices. However, extreme volatility of housing stock returns may impair the forecasting quality.

The endurance index of housing investor sentiment is easy to construct and use for forecasting housing stock returns. The demonstrated predictability of the index on housing stock returns in this study can have broad implications on housing-related business practices through providing an effective forecasting tool to investors and analysts of housing stocks, as well as housing policy-makers.

Despite different investor sentiment proxies suggested in the previous studies, few of them can effectively predict stock returns, due to some embedded limitations. Many increases and decreases inn prices cancel out each other during the trading day, as many unreliable sentiments cancel out each other. This dynamic process reveals not only investor sentiment but also resilience or endurance of sentiment. It is only long-lasting resilient sentiment that can be built in the closing price. It means that the only feasible way to use investor sentiment contained in stock prices to forecast future stock prices is to detach resilient investor sentiment from stock prices and construct an index of endurance of investor sentiment.

The purpose of this paper is to use data on new apartment offerings in the municipality of Sao Paulo, Brazil to illustrate its main claim that the hedonic direct method using time dummies as well as the simple average method include cyclical behavior of observables and non‐observables in a house price index that may overestimate or underestimate the actual change in house prices, well beyond the composition effects.

The paper proposes the use of alternative characteristics hedonic functions to compute alternative Laspeyres house price indexes that differentiate the sources of observable shocks in the index. The decomposition allows for the inclusion of level and cyclical behavior of sets of aggregate variables into the index.

The appropriate house price index should filter out real shocks that potentially affect the real estate sector. An index should capture nominal variation and incorporating real variation biases the measurement. Thus, the index is intended and able to buffer the bias spillover into the rest of economy. In the limited sample from the city of Sao Paulo, Brazil, the main finding is that real shocks and US foreign shocks give an upward bias in the house price index, while nominal shocks give mostly a downward bias. Real shocks make the index incorporate gains that should not be incorporated into the index, thus providing a noisy picture of the nominal variation in house prices.

The key contribution of this paper is to provide a framework for the construction of a house price index that filters out real shocks that potentially affect the real estate sector. An index should capture nominal variation and incorporating real variation biases the measurement. Those biases can spillover to the rest of the economy is a detrimental way. Thus, the index is intended and able to buffer the bias spillover into the rest of economy.

This paper argues that the methods of constructing house price indices for UK markets lag behind those employed in Europe, Australasia and North America. This is particularly evident in terms of the range and level of technical sophistication of the index construction methodologies. Importantly, the paper argues that the absence of reliable house price indicators undermines the decision‐making ability of policy makers and investors operating in urban housing markets. The paper suggests that this can, in part, be remedied by the construction of a system of local house price indices for British cities. The empirical research presents the first UK application of the repeat sales method to UK data. Indices are constructed for four cities and a range of diagnostic tests are used to establish the reliability and accuracy of the indices as a means of monitoring house price change. The research concludes by suggesting that the methods used here should be tested further on data from major metropolitan regions in England and Wales.

The paper aims to describe, construct, and compare alternative price indices for real estate in Malta over the period 1980‐2010.

The paper utilises the technique of hedonic regression analysis to construct four hedonic price indices. One of the constructed indices is based the unconstrained hedonic methodology. Two other indices are variants of the constrained hedonic technique, while the fourth consists of an imputed hedonic index. The hedonic indices are then compared to other 12 conventional indices, namely the Laspeyres, Paasche and Fisher indices (constant weight and chain linked) that are constructed by utilizing the mean and median house prices pertaining to 14 different types of houses.

All indices are found to move closely together, growing between six and seven times between 1980 and 2010. The average annual compound growth rate of the 16 indices was found to be 6.5126 percent. The paper also shows how the estimated hedonic coefficients can be used to construct regional price indices for different combinations of housing characteristics.

The paper builds on previous work related to house prices in Malta. Its main contribution is the construction of hedonic indices that are based on advertised prices that span over a relatively long period of 31 years, together with the construction of constant weight and chain linked Laspeyres, Paasche and Fisher indices.

The purpose of this paper is to examine if temporal aggregation matters in the construction of house price indices and to test the accuracy of alternative index construction methods.

Five index construction models based on the hedonic, repeat‐sales and hybrid methods are examined. The accuracy of the alternative index construction methods are examined using the mean squared error and out‐of‐sample technique. Monthly, quarterly, semi‐yearly and yearly indices are constructed for each of the methods and six null hypotheses are tested to examine the temporal aggregation effect.

Overall, the hedonic is the best method to use. While running separate regressions to estimate the index is best at the broader level of time aggregation like the annual, pooling data together and including time dummies to estimate the index is the best at the lower level of time aggregation. The repeat‐sales method is the least preferred method. The results also show that it is important to limit time to the lowest level of temporal aggregation when construction property price indices.

This paper provides alternative method, the mean squared error method based on an out‐of‐sample technique to evaluate the accuracy of alternative index construction methods.

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. However, the index method and level of temporal aggregation to use still remain unresolved in the index construction literature. This paper contributes to fill these gaps.

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.