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The repeat sales house price index (HPI) has been widely used to measure house price movements on the assumption that the quality of properties does not change over time. This study aims to develop a novel improvement-value adjusted repeat sales (IVARS) HPI to remedy the bias owing to the constant-quality assumption.

This study compares the performance of the IVARS model with the traditional hedonic price model and the repeat sales model by using half a million repeated sales pairs of housing transactions in the Auckland Region of New Zealand, and by a simulation approach.

The results demonstrate that using the information on improvement values from mass appraisal can significantly mitigate the time-varying attribute bias. Simulation analysis further reveals that if the improvement work done is not considered, the repeat sales HPI may be overestimated by 2.7% per annum. The more quality enhancement a property has, the more likely it is that the property will be resold.

This novel index may have the potential to enable the inclusion of home condition reporting in property value assessments prior to listing open market sales.

The novel IVARS index can help gauge house price movements with housing quality changes.

The aim of this paper is to present estimation results of hedonic price models as well as housing price indices for the Warsaw secondary market.

Three direct methods of constructing a hedonic price index and four indices that allow for quality adjustment are presented. The paper also discusses theoretical issues related to the estimation and interpretation of hedonic models.

It is shown that the imputation and the time dummy variable indices are subject to less variation than the characteristic price index. It is also shown that in comparison to the mean and the median, hedonic indices are less variable, which can be interpreted as partial control for quality changes in dwellings sold.

As this research project represents one of the first attempts of hedonic modelling applied to the Polish housing market, its results may be employed by appraisers to gain insight into behaviour of the Warsaw housing market. Practical implications focus on reliable measurement of house price dynamics in Poland. This paper supplies an appropriate methodology for addressing this question and offers empirical solutions.

Employment of hedonic models for construction of quality‐adjusted housing price indices has not yet been explored in Poland. The theoretical and practical aspects of hedonic indices presented in the paper open promising directions for the development of Polish statistics of real estate 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 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.

Commonly used rent indices are based on average developments or expert opinions. Such indices often suffer from compositional biases or low data coverage. The purpose of this paper is to overcome these challenges using the authors' approach.

The authors construct a quality-adjusted rent index for the office market in Oslo using detailed data from 14,171 rental contracts.

The authors show that compositional biases can have a large impact on rental price developments. By adding building-fixed effects to a standard hedonic regression model, the authors show that the explanatory power increases considerably. Furthermore, indices excluding location-specific information, or which include less granular location controls than at the building level, portray quite a different picture of rent developments than indices that do take this into account. The authors also exploit information on contract signature date and find that a more timely detection of turning points can be achieved by using the signature date instead of the more typically used start date of the lease.

The study is confined to Norwegian data, and an avenue for future research would be to explore if similar results are obtained for other countries. A weakness with the paper is that authors' do not observe quality changes over time, such as renovation. Controlling for time-varying and unit-specific attributes in hedonic models for the commercial real estate (CRE) market would be useful to purge indices further for compositional effects and unobserved heterogeneity. While the authors do control for building-fixed effects, there are additional variations within a building (floor, view, sunlight, etc.) that the authors do not capture. Studies that could control for this would certainly be welcome, both in order to estimate the value of such amenities and to see how it affects estimated rent developments. Another promising avenue for future research is to link data on rental contracts in the CRE market with firm-specific information in order to explore how firm profitability and liquidity may affect rental contracts.

The authors show that the hedonic index yields a sharper fall in rents after the global financial crisis and more muted developments in the period between 2013 and 2015 than the average rent index. The results show that rents have followed their estimated equilibrium closely and have re-adjusted quickly in periods of deviation. From a financial stability perspective, the risk of a sharp fall in rents is reduced because rents often are in line with their fundamentals.

The authors find that a more timely detection of turning points can be achieved by using information on the signature date. This is an important finding. The financial system is heavily exposed toward CRE, and timely detection of turning points is critical for policymakers.

The financial system is heavily exposed toward the commercial real estate market and timely detection of turning points is of major importance to policymakers. Finally, the authors use our quality-adjusted rent index as the dependent variable in an error correction model. The authors find that employment and stock of offices are important explanatory variables. Moreover, the results show that rents have followed their estimated equilibrium path.

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.

Aging in Japan is advancing faster than in other major developed nations, and this is expected to have substantial effects on the country’s economic systems, including its social security system. What kind of effect will the falling birth rate, aging society and declining population have on the real estate market? Will the often mentioned real estate price asset meltdown really occur? The purpose of this paper is to address these questions by investigating how much demographic factors affected real estate prices in Japan and the USA.

The authors use regional panel data for Japan and the USA real estate prices and estimate the effects of demographic factors, such as dependency ratio, i.e. the ratio of population aged 65+ to population aged 20-64. For Japan, as no region-by-region quality-adjusted housing price indexes covering the entire country exist, data are constructed by conducting quality adjustment using hedonic regression.

Both in Japan and the USA, real estate prices in a region are inversely correlated with the old age dependency ratio in that region, and positively correlated with the total number of population in that region. The demographic factor had a greater impact on real estate prices in Japan than in the USA. For Japan, it was also found that demographic impact on land prices will be −2.4 per cent per year in 2012-2040, while it was −3.7 per cent per year in 1976-2010, suggesting that aging will continue to have downward pressure on land prices over the next 30 years, although the demographic impact will be slightly smaller than it was in 1976-2010, as the old age dependency ratio will not increase as much as it did before.

Japan’s regional panel data are newly constructed based on a hedonic approach. Analyzing the effect of dependency ratio for Japan and the USA panel data is a new challenge. Forecasting future impact of demographic factor on Japan’s land prices based on the population forecast is a new challenge.

The purpose of this paper is to decompose and measure the microstructure of property investment returns for Tokyo’s residential property markets in as much detail as possible in comparison with office market.

Using enterprise value data for property investment trust companies composed of share prices available on capital markets, this study proposed a method of estimating property investment returns corresponding to changes in capital markets, and clarified the distortion in capitalization rate that are formed based on property appraisal prices.

The results for residential property showed that as building floor space increased, income and price increased while the discount rate decreased. In particular, a higher return could be obtained from office property than residential property by investing in larger-scale properties. Building age lowered asset price and income for both residential and office property, especially for residential property.

In Japan, investors believe that investment returns are high for properties close to the city centre, relatively new properties and those with large design or floor space. Therefore, this study first measured how asset prices, income and asset price–income ratios that comprise property investment returns change based on differences in these property characteristics. Second, the reliability/distortion of information that can be observed on the property investment market was measured. Furthermore, there was a significant divergence between discount rates and risk premiums formed by asset or space markets versus capital markets.

The differences of discount rate and risk premium formed by asset markets versus capital markets indicate that appraisal prices have biases. Thus, when it comes to property investment decisions, it is essential to make active use not just of property investment returns based on appraisal prices formed by asset markets but also information formed by capital markets.

A greater difference was generated in a shrinking market, suggesting that analysing property returns estimated on asset market information alone could lead to erroneous investment decisions.

This research is the first to use the enterprise value data from real estate investment trust companies composed of share prices available on capital markets for calculating discount rate and risk premium in property market.

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.

An economic indicator faces two requirements. It should be reported in a timely manner and should not be significantly altered afterward to avoid erroneous messages. At the same time, it should reflect changing market conditions constantly and appropriately. These requirements are particularly challenging for housing price indices, since housing markets are subject to large temporal/seasonal changes and occasional structural changes. The purpose of this paper is to estimate a hedonic price index of condominiums of Tokyo, taking account of seasonal sample selection biases and structural changes in a way it enables us to report the index in a manner which is timely and not subject to change after reporting.

The paper proposes an overlapping‐period hedonic model (OPHM), in which a hedonic price index is calculated every month based on data in the “window” of a year ending this month (this month and previous 11 months). It also estimates standard hedonic housing price indexes under alternative assumptions: no structural change (“structurally restricted”: restricted hedonic model) and different structure for every month (“structurally unrestricted”: unrestricted hedonic model).

Results suggest that the structure of the housing market, including seasonality, changes over time, and these changes occur continuously over time. It is also demonstrated that structurally restricted indices that do not account for structural changes involve a large time lag compared with indices that do account for structural changes during periods with significant price fluctuations.

Following the financial crisis triggered by the US housing market, housing price index guidelines are currently being developed, with the United Nations, International Monetary Fund, and Organization for Economic Co‐operation and Development leading the way. These guidelines recommend that indices be estimated based on the hedonic method. We believe that the hedonic method proposed here will serve as a reference for countries that develop hedonic method‐based housing price indices in future.

In the many studies involving conventional housing price indices, whether those using the repeat‐sales method or hedonic method, there are few that have analyzed the problem of market structural changes. This paper is the first to construct a large database and systematically estimate the effect that changes in market structure have on housing price indices.