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In the late 20th century, homeownership became entrenched in a wider societal project that sought to transform the economy and increase social inclusion. This project focused on mortgaged owner-occupation as a means not only to acquire a stable home, but also to realise greater economic security via asset accumulation. The underlying ideology featured an implicit promise that homeownership would be widespread, equalising and secure. Despite transformations in market conditions, such narratives have continued to underscore policy approaches and housing marketisation. This article directly confronts this promise. It first unpacks its key tenets before investigating their currency across three classic ‘homeowner societies’: the US, the UK and Australia. Our empirical findings reveal declining access to homeownership, increasing inequalities in concentrations of housing wealth and intensifying house-price volatility undermining asset security. The article contends that the imperative of homeownership that has sustained housing policy since the 1970s may be increasingly considered a ‘false promise’. Our analyses expose contemporary housing market dynamics that instead appear to enhance inequality and insecurity. 在20世纪后期，房屋所有权在一个更广泛的社会项目中变得根深蒂固，这个项目寻求经济转型和提升社会包容度。该项目强调抵押房主自住，这不仅作为获得稳定住房的一种手段，也作为通过资产积累提升经济安全的一种手段。这其中潜在的意识形态所强调的是这样一个期许：房屋所有权将是广泛的、平等的和安全的。尽管市场环境发生了变化，但这种论述继续强调政策方法和住房市场化。本文直接质疑这个期许。在对这些主要信条在美国、英国和澳大利亚这三个典型的“房主社会”的现实体现进行调查之前，我们首先对这些信条进行了解析。我们的经验研究结果显示，获得住房所有权的机会在减少，住房财富集中的不平等在加剧，而破坏资产安全的房价波动则在加剧。本文认为，自20世纪70年代以来一直作为住房政策基石的房屋所有权必要性理论可能会越来越被视为一种“错误的期许”。我们的分析揭示了当代住房市场的动态，而这种动态似乎加剧了不平等和不安全。

This paper develops an artificial intelligence based automated valuation model (AI-AVM) using the boosting tree ensemble technique to predict housing prices in Singapore. We use more than 300,000 private and public housing transactions in Singapore for the period from 1995 to 2017 in the training of the AI-AVM models. The boosting model is the best predictive model that produce the most robust and accurate predictions for housing prices compared to the decision tree and multiple regression analysis (MRA) models. The boosting AI-AVM models explain 91.33% and 94.28% of the price variances, and keep the mean absolute percentage errors at 8.55% and 5.34% for the public housing market and the private housing market, respectively. When subject the AI-AVM to the out-of-sample forecasting using the 2018 housing sale samples, the prediction errors remain within a narrow range of between 5% and 9%.

This article identifies proxies which account for the impacts that the Airbnb platform is having on housing in Greater London. We identify these by analysing the relationships between possible Airbnb misuse and the attributes of housing in the same locations. We assume misuse when listings of entire properties within the Airbnb platform do not conform with local regulations and where hosts who offer such housing have multiple listings. In particular, we examine (1) the dwelling type based on building typology; (2) the type of housing tenure, whether it is owned or rented; and (3) the spatial distribution of changes in rent payable. Three important findings emerge from our analysis. First, based on 2018 data, we estimate that more than 2% of all properties in London, and up to 7% in some local areas are being misused through Airbnb as short-term holiday rentals. Second, the location of these particular Airbnb rentals is negatively correlated with the diversity of dwelling types and positively correlated with dwelling type such as an apartment (or flat) in areas of high private rental stock. Last, we show that a 100% increase in the density of possible Airbnb misuse can be associated with up to an 8% increase in unit rental price per-bedroom per-week, an equivalent to up to an average of £90 price increase per year. Finally, we discuss how this type of analysis can help build instruments to inform policies associated with the platform economy in relation to increasing polarisation in the London housing market.

How does one tell when rapid growth in house prices is caused by fundamental factors of supply and demand and when it is an unsustainable bubble? In this paper, we explain how to assess the state of house prices—both whether there is a bubble and what underlying factors support housing demand—in a way that is grounded in economic theory. In doing so, we correct four common fallacies about the costliness of the housing market. For a number of reasons, conventional metrics for assessing pricing in the housing market such as price-to-rent ratios or price-to-income ratios generally fail to reflect accurately the state of housing costs. To the eyes of analysts employing such measures, housing markets can appear “exuberant” even when houses are in fact reasonably priced. We construct a measure for evaluating the cost of home owning that is standard for economists—the imputed annual rental cost of owning a home, a variant of the user cost of housing—and apply it to 25 years of history across a wide variety of housing markets. This calculation enables us to estimate the time pattern of housing costs within a market. As of the end of 2004, our analysis reveals little evidence of a housing bubble.

PurposeChinese housing market has been growing fast during the past decade, and price-related forecasting has turned to be an important issue to various market participants, including the people, investors and policy makers. Here, the authors approach this issue by researching neural networks for rent index forecasting from 10 major cities for March 2012 to May 2020. The authors aim at building simple and accurate neural networks to contribute to pure technical forecasting of the Chinese rental housing market.Design/methodology/approachTo facilitate the analysis, the authors examine different model settings over the algorithm, delay, hidden neuron and data spitting ratio.FindingsThe authors reach a rather simple neural network with six delays and two hidden neurons, which leads to stable performance of 1.4% average relative root mean square error across the ten cities for the training, validation and testing phases.Originality/valueThe results might be used on a standalone basis or combined with fundamental forecasting to form perspectives of rent price trends and conduct policy analysis.

In this essay, we review the basic economics of housing supply and the functioning of US housing markets to better understand the distribution of home prices, household wealth, and the spatial distribution of people across markets. We employ a cost-based approach to gauge whether a housing market is delivering appropriately priced units. Specifically, we investigate whether market prices (roughly) equal the costs of producing the housing unit. If so, the market is well-functioning in the sense that it efficiently delivers housing units at their production cost. The gap between price and production cost can be understood as a regulatory tax. The available evidence suggests, but does not definitively prove, that the implicit tax on development created by housing regulations is higher in many areas than any reasonable negative externalities associated with new construction. We discuss two main effects of developments in housing prices: on patterns of household wealth and on the incentives for relocation to high-wage, high-productivity areas. Finally, we turn to policy implications.

We analyze the role of macroeconomic uncertainty in predicting synchronization in housing price movements across all the United States (US) states plus District of Columbia (DC). We first use a Bayesian dynamic factor model to decompose the house price movements into a national, four regional (Northeast, South, Midwest, and West), and state-specific factors. We then study the ability of macroeconomic uncertainty in forecasting the comovements in housing prices, by controlling for a wide-array of predictors, such as factors derived from a large macroeconomic dataset, oil shocks, and financial market-related uncertainties. To accommodate for multiple predictors and nonlinearities, we take a machine learning approach of random forests. Our results provide strong evidence of forecastability of the national house price factor based on the information content of macroeconomic uncertainties over and above the other predictors. This result also carries over, albeit by a varying degree, to the factors associated with the four census regions, and the overall house price growth of the US economy. Moreover, macroeconomic uncertainty is found to have predictive content for (stochastic) volatility of the national factor and aggregate US house price. Our results have important implications for policymakers and investors.

Housing markets display several correlations to multiple economic sectors of an economy. Their enormous impact on economies’ health, wealth, and stability is uncontroversial. Interestingly, the forms of financing residential property vary widely between the different countries in terms of both, the available product types and the institutions offering them. This research examines the implications of different financial intermediaries on housing market cycles with special emphasis on two institutional types, conventional banks and building and loan associations. Introducing a heterogeneous agent-based model, the interactions of buyers, sellers, and the two types of credit institutions are assessed. Heterogeneous economic principles and expectations of agents create endogenous market conditions which are strongly influenced by the lending practices of financial intermediaries.Focusing primarily on collateral values to decide about lending, conventional banks may contribute to volatile housing markets which are prone to recessions. Building and loan associations, on the other hand, rely to a greater extent on endogenously created borrower information. Thus, they are able to cushion the volatility of house prices caused by procyclical mortgage lending of conventional banks and increase the stability of the housing market. Simulations show that the most stable market conditions are attained if both types of financial intermediaries serve the mortgage lending market jointly. Furthermore, transaction and homeownership rates are the highest in this market setting. These findings advocate in favor of diversified financial markets.

Purpose This paper aims to improve the housing affordability by measuring the housing affordability in a resource-rich economy and studying the impact of implementing new policies. Design/methodology/approach This paper seeks to test the impact of new policies introduced to the Kuwaiti housing market to improve affordability. In 2008, the Kuwaiti parliament introduced two policies: a tax on empty lands and, forbidding companies to own or develop residential lands or houses. Findings By constructing the housing affordability index and the price-to-income multiplier using observations from 2004 until 2017, it has been found that affordability has worsened over time regardless of the new policies introduced in 2008. Housing in Kuwait became “severely unaffordable” (equivalent to London in the UK, San Diego in USA and Toronto in Canada). Originality/value Even with its unique condition, as a rich country, small population and availability of white land and other resources, the affordability worsened over time. Introducing new policies without solving the central issue of housing supply challenges seems not worth it. This paper is the first of its kind on the Kuwait housing market, and it provides a valuable foundation for future research on this market and similar markets in the region.

The cross-regional dependency in the UK housing market is analysed using regional house price indices. In this article, a network approach based on partial correlations is proposed, along with rolling-window analysis to consider potential time-varying dependency. The results show that house prices in the outer South East region have the strongest influence on regional housing market interactions in the UK. This influence is stronger when the markets are highly interconnected, whereas the house prices in London have the strongest influence when the UK regional housing markets are relatively less connected. 我们通过地区房价指数分析了英国住房市场的跨地区依赖性。本文提出了一种基于部分相关的网络方法，并结合滚动窗口分析来考察潜在的时变相关性。结果显示，在英国，东南部外围地区的房价对地区住房市场互动的影响最大。当市场高度关联时，这种影响更大，而当英国地区住房市场关联度相对较低时，伦敦房价的影响最大。