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This paper aims to explore the impact of the joint operation of affordability policies and whether their impact is meaningful relative to the size of the affordability crisis. Its purpose is to construct the features of a comprehensive policy package linked to a measurable outcome: solving the affordability crisis in Auckland. This study investigates the scale and nature of an affordability package that responds to three research and policy questions: What should the rate of annual growth of affordable housing be to solve the affordability crisis? Consequently, how long would it take to solve it? And how much would that policy package cost?

In this paper, the authors argue that the disjoint operation of affordability policies is one of the reasons why their impacts relative to the size of the affordability crisis has been small or negligible. Furthermore, this study demonstrates that affordability is as much about raising households’ incomes as it is about lowering housing prices. This study uses a modelling-based framework to simulate some of the levers that local and central governments have to affect housing prices and affordability: direct intervention on the supply-side and subsidies. Specifically, this simulates retention-and-targeting (RT) programs and subsidies to raise deposits via shared ownership (SO) schemes.

This study finds that solving the affordability crisis would take a decade if the supply of affordable housing increases by at least 45% annually. With the introduction of RT and SO programs, it could still be solved within a decade, where the required growth rate decreases to 35%. However, for growth rates between 5% and 10%, the policy goal is met in approximately 40 years, and the SO program becomes exceedingly expensive.

Housing affordability is one of the hottest policy issues in New Zealand and the developed world. In the past decade, a number of affordability policies have been introduced with limited success due to their lack of interoperability and siloed efforts. Results in this paper should be interpreted as the boundaries of what is feasible and realistic in the realm of affordability policies. Therefore, its contribution relies on investigating the multiple effects if the financial, administrative and political barriers to RT and SO programs could be overcome. Its scope is a blueprint for the design of policies in other cities where unaffordability has become extreme.

Since the 1980s UK academics have promoted the use of multiple regression analysis in property valuation. Recently, however, there has been growing recognition that regression models will be subject to aggregation bias if they fail to accommodate the existence of housing market segmentation (submarkets). In this study, we compare the empirical performance of a standard hedonic house price regression model for the city of Glasgow with a segmented model which recognises the importance of understanding the underlying market structure and, in particular, the existence of submarkets for different dwelling types. The results show that the (weighted) standard error of the segmented model is significantly lower than that of the market wide model. Consequently, we propose a two‐stage approach to the application of MRA techniques to residential valuation. First, following traditional institutional analysis of housing markets, the market should be subdivided into distinct structurally differentiated market segments. These segments can usefully be identified by principal components factor analysis which allows the identification of the most important common components in the housing bundle. Second, separate house price equations should be estimated for each market segment. Although the best‐fit equation may vary from sector to sector this is likely to reflect the behavioural realities of the property market, and will provide the basis for more accurate valuations.

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 aims to investigate the factors that contribute to the changes of house prices including ethnic factors. Australia is a multicultural country with diversified ethnicities. The median price of established houses (unstratified) in Sydney has reached a new record high of $910,000 in December 2015, increasing around 58.2 per cent from March 2011 [Australian Bureau of Statistics (ABS), 2015a]. However, the prices of some suburbs have increased more than prices of others.

Six suburbs that represent ethnic majority originally including White, India and China will be selected as pilot studies. Hedonic regression analysis will be applied for the analysis based on 2001, 2006 and 2011 census data.

It is found that the main drivers of house prices are the dwelling physical characteristics and accessibility to convenient transportation. The level of household income also plays an important role. However, the impact of changes of ethnic on changes of prices is not significant.

The study adds to the growing literature on the ethnicity changes on dwelling prices and is important for understanding whether some of the clusters of ethnic concentration or segregation effects property markets. This study is significant in its understanding of the main characteristics of ethnic changes of suburbs in Sydney.

An implication is that policy makers can attract different ethnic groups and encourage multicultural communities when they formulate housing and planning policies.

The relationship between ethnicity and house price appreciation is not extensively studied in Australia. This research contributes to the literature on the effects of ethnic changes on house prices and implications of policy formulation to encourage multicultural communities.

The introduction of higher density housing development within suburban areas has been favoured by state governments in Australia as a means of improving the efficiency of land use, reducing the costs associated with the delivery of government services and promoting home ownership. However it has been hypothesised that such development may have a negative impact on neighbourhood social structure, for example reducing diversity as measured by economic status and family makeup or in local housing market performance as measured by price. This paper aims to test this hypothesis.

The methodology employs a quantitative approach with principal components analysis used to capture the main social structure of the Adelaide Statistical Division. Social constructs, the product of principal components analysis, are used to measure outcomes of higher density development as measured by community or household change.

The results in this paper show that densification has had significant impact on certain neighbourhoods in the Adelaide Statistical Division notably in relation to their built form but not necessarily in neighbourhood structure or housing market performance.

The findings are significant in highlighting that increasing medium densities and improving tenure mix may not necessarily improve the opportunities for socio‐economic mix or for cultural diversity with implications for policy makers seeking to follow strategies based on the promotion of mixed communities.

This paper seeks to add new research on the outcomes of higher density development in Australia in three ways. First, social constructs, the product of principal components analysis, are used to measure outcomes of higher density development as measured by community or household change. Second, the paper investigates the development at the local level where impacts are likely to be most important. Third, the analysis identifies a before and after scenario for those suburbs where higher density development has been most significant.

The purpose of this study is to highlight the determinants of market rents and to build a hedonic market rent index for each urban area and rental sector in France for the period 1970–2013. The authors also analyse the market rent dynamics over this period, with a special attention to the turning points in the French housing policy.

For this purpose, the authors implement a hedonic model, called stratified time dummy variable, using the Box–Cox transformation as a functional form.

The contribution of this study to the housing research is threefold: First, the study improves our understanding of the French’s rental submarket specificities and their valuation. It sheds new light on the determinants of rents. Second, this study builds a hedonic market rent index over the period 1970–2013 for each geographical and sectoral segment (Paris urban area, urban areas of more and less than 100,000 inhabitants and private and public rental sectors). Third, this study explains rent dynamics focusing on the turning points in the French housing policy.

Finally, the authors provide the first long-term market rent index in France by submarket (geographical and sectoral). In the case of the French market, no long-term market rent exists. The only long series available is an indexed rent.

This paper, a pioneering one in the Israeli context, aims to augment the research literature on school quality and housing prices by examining the effect of primary-school performance on local property values. It focuses on the main question whether the release of students’ test scores offered households a new source of information with which they could evaluate the quality of schools, thereby affecting local housing markets.

Several models that examine a variety of transactions, schools and locality characteristics that affect house prices are estimated. Using different administrative sources of information, a wide array of socioeconomic characteristics of students, parents and homebuyers, as well as locality features, is constructed and merged. This information, combined with students’ scores on Meitzav exams (standardized student achievement tests) in 2009-2012 and house prices, illuminates the relationship between student achievements and the prices of houses purchased within the defined attendance zones.

Student achievements, mainly in the state education system, are found to have a positive and statistically significant effect on housing prices. Accurate information published about a certain school that showed much stronger achievements than those yielded by information attainable about the same school before school-level publication, does contribute to boost house prices in the post-publication period. The socioeconomic background of the students’ parents was found to have a significant effect on house prices. The premium for housing value is much higher in the most prestigious, prime demand districts, in which the housing supply is limited and the housing price level is higher than in that the peripheral districts.

This study not only breaks new ground in the Israeli context but also contributes to the existing literature, by investigating the relation between publishing students’ scores and property values near the same schools, on a national scale. Given that the housing price dynamics and the spatial differentiation of housing stock are extremely hot issues in many European cities, the results of this study could serve as an important tool for better understanding the housing price responses to market incentives, resulting in specific patterns in local housing markets. This paper could be thus applicable in housing policy outline, urban design and planning.

This is the first article to apply a machine learning approach to the analysis of time on market on real estate markets.

The random survival forest approach is introduced to the real estate market. The most important predictors of time on market are revealed and it is analyzed how the survival probability of residential rental apartments responds to these major characteristics.

Results show that price, living area, construction year, year of listing and the distances to the next hairdresser, bakery and city center have the greatest impact on the marketing time of residential apartments. The time on market for an apartment in Munich is lowest at a price of 750 € per month, an area of 60 m2, built in 1985 and is in a range of 200–400 meters from the important amenities.

The findings might be interesting for private and institutional investors to derive real estate investment decisions and implications for portfolio management strategies and ultimately to minimize cash-flow failure.

Although machine learning algorithms have been applied frequently on the real estate market for the analysis of prices, its application for examining time on market is completely novel. This is the first paper to apply a machine learning approach to survival analysis on the real estate market.

This paper presents a mixture of linear models (or hedonic regressions) for defining housing submarkets. Two different mixture models are considered: the first model allows all the regression coefficients to vary among the clusters (random coefficients); and the second model allows only the intercept term to change (random intercept). The model with a random intercept can be seen as a linear mixed model where the random effects distribution is estimated via non-parametric maximum likelihood (NPML). The models are illustrated using a real data set of 293 properties in Pamplona, Spain. These mixture models provide a classification of the dwellings into homogeneous groups that determine the structure of the submarkets.

This study aims to examine the impact of local air pollution on housing prices in Lebanon.

The authors apply a hedonic pricing approach using a unique data set from Lebanon. To account for non-linearities in pricing, the authors use three different functional regression forms for the hedonic model approach. The authors also deal with potential omitted variable bias by estimating a hedonic frontier specification.

The authors find that, in all specifications, air pollution negatively and significantly affects housing prices. The estimated marginal willingness to pay for a one microgram per cubic meter change in particulate matter (PM10) concentration ranges between 2.88% and 3.18% of mean housing prices. The authors also provide evidence of a negative pricing gradient away from the city center, landing support for the monocentric urban development hypothesis.

Given the lack of a data set linking household socioeconomic characteristics with housing data, the authors only consider the first-stage hedonic model.

The proposed hedonic pricing regression approximates a housing pricing equation that can be used by policymakers.

The findings suggest that pollution is a significant factor in household behavior in Lebanon.

This paper adds to the scant literature studying the effects of air pollution on housing prices in developing countries. To the best of the authors’ knowledge, this is the first paper to study the impact of pollution on housing prices in a country in the Middle East and North Africa Region.