Speculative housing markets and rent control: insights from nonlinear economic dynamics

1 Housing Markets and Housing Institutions in a Comparative Context.- Housing Is Peculiar.- Housing Policies Are Special.- The Rationale of this Book.- Metropolitan Markets in National Economies.- A Taxonomy of Housing Policies.- Conclusion.- 2 The Swedish Housing Market: Development and Institutional Setting Alex Anas.- The State, the Counties, and the Municipalities.- Housing Policy.- The Planning System.- Land Use and the Ownership, Supply, and Pricing of Land.- The Housing Stock, Housing Production, and the Building Sector.- Financing of New Construction and Modernization.- Pricing, Rent Control, Rent Pooling, and Rent Negotiations.- The Public Queue: The Case of Greater Stockholm.- Swapping, Black Markets, Mobility, and Household Formation.- Housing Allowances.- Housing and Income Tax.- Conclusions.- 3 The Finnish Housing Market: Structure, Institutions, and Policy Issues.- Historical Background.- Urban Land.- Administration of Housing and Urban Planning.- Housing Production.- Development of the Dwelling Stock and Housing Finance.- Pricing of Housing.- Obtaining Shelter in the Helsinki Metropolitan Area.- Mobility, Household Formation, and the Housing Market.- Housing Allowances.- Housing and the Income and Wealth Taxes.- Conclusions.- 4 The Functioning of the Housing Market in Amsterdam.- An Institutional-Economic Framework.- The City of Amsterdam.- The Development of the Housing System.- Population, Housing, and Mobility in Amsterdam.- The Planning System.- Pricing and Financing.- The Allocation of Households to Dwellings.- The Black Market: Squatting.- Conclusions.- 5 Housing in San Francisco: Shelter in the Market Economy.- The San Francisco Bay Area.- Federal and State Housing Policy.- Regionalism and Localism in Bay Area Land Use and Development 195 Summary and Conclusions.- 6 Analysis of the Housing Sector, The Housing Market, and Housing Policy in the Budapest Metropolitan Area.- The Budapest Metropolitan Area in the Settlement System of Hungary and Central Europe.- Development of the Housing Sector in Budapest.- Housing Quality and the Evolution of Financing.- Conclusion.- 7 The Vienna Housing Market: Structure, Problems, and Policies.- The Structure of the Housing Market in Metropolitan Vienna.- The Governmental Role in the Housing Market.- Conclusion: Major Impacts of Housing Policies.- 8 Glasgow: From Mean City to Miles Better.- The Message and the Medium.- Time's Arrow.- New Pluralism.- Remaking Council Housing.- Conclusion.

Housing and Labor Market Distortions in Poland: Linkages and Policy Implications

The state of development of rental markets as a genuine alternative to home-ownership stands out as a particularly relevant institutional factor shaping the outcome of the housing market and playing a balancing role and alleviating house price pressures. This is especially the case when it proves to be an affordable platform for young and low-income households, providing them with a viable alternative to a hasty first step into the property ladder. In order to help policymakers develop a sizeable private rental market acting as an attenuating factor of housing prices volatility, it is important to depict the relevant dimensions of the rental market regulation and assess their likely impact on the aggregate housing market. Against this background, this paper first develops a two-dimensional indicator on rental market regulation, covering for rent controls and the tenant-landlord relationship. The resulting indices are put to the test by assessing their impact on housing prices. According to this analysis, an efficient, fair and swift judicial system appears as a necessary step towards unlocking rental markets full potential. Moreover, rent controls appear to have a significant destabilizing impact on the aggregate housing market, increasing the volatility of house prices when confronted with different shocks. Finally, qualitative aspects of the tenancy contract negotiation do not have a first-hand impact on housing market dynamics.

The study deals with urban economic residential location modelling of Budapest, Hungary, using a pragmatical neural network approach. First the analysis pertains to the city level spatial housing market structure through identifying a number of factors related to housing supply, demand and price. After that, the analysis is zoomed in to involve the dynamics of two selected inner city neighbourhoods: the middle-parts of the districts VIII (JÛzsefv·ros) and IX (Ferencv·ros) respectively. These areas in the south-eastern part of the inner city have both received attention as subjects for substantial rehabilitation in recent decades. The two districts are adjacent, but different: the former is stigmatized in all discourse although it comprises a great variety of microlocations and also housing stock; the latter district in turn is more homogeneous, partly gentrified area and undoubtedly the most dynamic neighbourhood in the city with the best quality apartments on the Pest side. Earlier results (reported elsewhere) based on a small set of individual mortgage valuations suggested the Budapest housing market to be spatially and functionally extremely fragmented, as a mosaic of various house types, age-categories and price-levels, as well as micro-locations, could be identified. A follow-up analysis of street- and district-wise aggregated data with the same neural network modelling techniques, namely the self-organizing map (the SOM) and the learning vector quantification (the LVQ), shows further evidence about the balance between physical and socio-demographic characteristics and house price levels. The new results are synchronised with the earlier findings: also here the administrative district is found less important determinant of the market position of the dwelling than its price, type and immediate vicinity. After that the case-study areas were modelled using the SOM for each crosssection during the observed six-year period from 1997 to 2002. Two types of dynamics could be identified, as the housing market dynamics in the selected ëslice' of the urban area has differentiated the micro-locations in terms of house price escalations on one hand, and in terms of changes in the urban structure on the other. Above all the evidence reveals how the housing market development is related to the most localized processes of social and physical upgrading taking place in an urban setting.

Heterogeneous expectations, boom-bust housing cycles, and supply conditions: A nonlinear economic dynamics approach

Institute of University of I ' Business and California at I Economic Research Berkeley FISHER CENTER FOR REAL ESTATE AND URBAN ECONOMICS WORKING PAPER SERIES WORKING PAPER NO. 84-83 THE INTERJURISDICTIONAL EFFECTS OF GROWTH CONTROLS ON HOUSING PRICES L By These papers are preliminary in nature: their purpose is to - stimulate discussion and LAWRENCE KATZ comment. Therefore, they . KENNETH T_ ROSEN are not to be cited or quoted in any publication without the ex- press permission of the author. WALTER A. HAAS SCHOOL OF BUSINESS _

PurposeThe purpose of this paper is to study the effect of the 2008 global financial crisis on housing market dynamics in an emerging economy like India using quarterly data (Q4 2008–2009 to Q1 2018–2019). The study explores the extent of linkages between housing prices, monetary policy and financial stability by explaining the nature of the shocks to the housing sector and the degree of impact of those shocks; the possibility of adverse feedback loop which is beyond the natural levels; and the usefulness of explicit and direct role of monetary policy for the housing market stability, which was the loudest demand immediately after the crisis.Design/methodology/approachThe paper follows a three-step methodology: data transformations, a variable selection process “general-to-specific modelling” with the help of OxMetrics 6 Package, and vector autoregressive modelling with the help of EViews 10. F-test was used to describe the short-term relationships between the variables. Impulse response and variance decomposition were used to explain the type of relationship (negative or positive) and the period of the relationships, respectively.FindingsThe study finds that the housing sector is sensitive to the monetary policy shocks, whereas the contribution of the housing market shocks to the fluctuations in other market variables is not substantial, though not negligible. As far as the nature of the shocks is concerned, the observed dynamics in the real house prices are diverging from their fundamental levels. The housing market shocks are more or less static; it rules out the chances for a self-reinforcing feedback loop with the existing setup.Research limitations/implicationsThe study concludes that the observed dynamics in the real house prices are diverging from their fundamental levels. Given the limitation, the researchers could extend this study by decomposing the part of the risk to the sector contributed by the other drivers, which may be inherent imperfections in housing markets, weak and unreliable wealth effect, and the presence of behavioural biases.Practical implicationsThe present study finds countercyclical measures to be more useful for this sector as compared to the forward-looking monetary policy reforms in this sector. The central bank in India should continue to refrain from responding directly to the housing sector fluctuations. Investors can enjoy investing in the housing sector without any fear of the crisis as of now. The effect of speculation is small but not negligible, which enjoins the investors and the policy-makers to remain watchful. Interest rate, money supply and inflation lead (Granger-cause) the housing prices. This information is relevant for spending and investment decisions.Social implicationsThe study feels that banks should avoid using monetary policy to balance the house prices. This will be beneficial both for the economy and the society, as any change in monetary policy to especially curb out surging housing prices may adversely affect the output, and finally, may lead to the deflation. The fear of deflation may cause devastating economic, financial and social effects.Originality/valueThe study contributes to the literature by shedding some new insights about the interrelationship between macroeconomic variables, housing prices and financial stability in the aftermath of the 2008–2009 financial crisis. Such types of studies are absent from emerging markets, particularly from India.

Recent housing‐market studies have modeled slow stock and price adjustment with some success. However, the empirical procedures used in these models break down if housing stocks or prices are driven by stochastic growth. In this paper I suggest an error‐correction model for analyzing housing supply and demand under conditions of stochastic growth for a regional housing market. The model is applied to the housing market in Boulder, Colorado from 1981 through 1995—a period of rapid growth in housing values in the area. Long‐run housing supply and demand are shown to be inelastic with respect to changes in the price of housing. The results indicate that developers respond more accurately to housing‐market disequilibrium attributable to supply‐side disturbances than to disturbances generated by changes in the demand for housing. On the other hand, price appreciation is driven primarily by demand disturbances.

Purpose Increasing number of residents in rapidly urbanizing cities of sub-Saharan African are experiencing growing exclusion. This study aims to examine socio-spatial exclusion in housing markets and seeks to understand policy and market-based trajectories of exclusion in Hawassa City, Ethiopia. Design/methodology/approach A mixed-methods approach was used, combining primary and secondary data. Interviews, document reviews and housing price data were gathered. Tender-based data for 1,927 plots over a 10-year period was obtained from the city’s land development department. In addition, 120 Global Positioning System-based housing price data points were randomly collected, considering location in X-Y coordinates, housing types, distance from the central business district (CBD) and sale repetition. Data were analyzed thematically, and spatial patterns of housing prices were mapped using techniques like inverse distance weighted interpolation and Moran’s clustering in ArcGIS. Findings The study revealed a growing trend in housing prices driven by policy and market forces, leading to spatial exclusion. Gaps in housing policy drove demand for peri-urban areas, fueling black markets and gentrification, which exacerbated socio-spatial exclusion over time. Housing prices in formal and informal markets show a converging trend, driven by neoliberal policies and market forces prioritizing urban land values. Housing prices exhibit spatial disparity, with significant hikes observed at 4 km from the CBD, reflecting the impact of location and policy. The findings demonstrate an evolving polycentric urban hubs driving socio-spatial exclusion in housing markets. Research limitations/implications Based on the findings, this study calls for further research on viable housing policies to promote a socio-spatially inclusive housing market in rapidly urbanizing sub-Saharan African cities. This requires analyzing robust housing market data (both spatial and nonspatial) across inner-city and peri-urban areas, exploring innovative land management approaches and understanding the housing realities of low-income urban residents using ethnographic approaches, including deeper socioeconomic and political forces. Practical implications The study has implications for promoting inclusive housing policies and market options in rapidly urbanizing cities of sub-Saharan Africa, supported by robust housing/land market data, primarily targeting on the housing realities of households at the lower end of the market. Rapidly urbanizing cities could also promote urban entrepreneurialism by advocating policies that create markets to drive socio-spatial inclusivity. Originality/value This manuscript presents a scientific case on the social and spatial dimensions of housing markets within the context of embedded housing policy frameworks and market forces in sub-Saharan Africa. Drawing on a case study from Hawassa, Ethiopia, it also provides empirical data and sheds light on the exclusion/inclusion literature and urban rent theory. Based on the findings, the study also emphasizes the emerging implications – growing gentrification and socio-spatial exclusion – reflecting neoliberal urbanism in the housing markets of Hawassa City.

We combine a standard stock-flow housing market model, incorporating explicit relationships between house prices, the housing stock, and the rent level, with a parsimonious expectation formation scheme of housing market investors, reflecting an evolving mix of extrapolative and regressive expectation rules. The model results in a two-dimensional discrete-time nonlinear dynamical system. Based on realistic parameters, the model is able to generate endogenous boom-bust housing market dynamics with lasting periods of overvaluation and overbuilding. We thus exploit our model to investigate how real forces, in particular supply conditions, interact with expectations-driven housing market fluctuations.

Comment

Purpose– This paper aims to pose an important starting point for the application of the search-and-matching models to real estate appraisals, thus reducing the “gap” between practitioners and academicians. Due to relevant trading frictions, the search-and-matching framework has become the benchmark theoretical model of the housing market. Starting from the large related literature, this paper develops a simplified approach to modelling the frictions that focuses on the direct relationship between house price and market tightness (a common feature only for the labour market matching models). The characterization of the equilibrium through two main variables simplifies the analysis and allows using the theoretical model for empirical purposes, namely, the real estate appraisals.Design/methodology/approach– This work is both theoretical and empirical. Theoretically, a long-run equilibrium model with a positive share of vacant houses and home seekers is determined along with price and market tightness. Also, the conditions of existence and uniqueness of the steady-state equilibrium are determined. Unlike most of the search-and-matching models in the housing literature, the out-of-the steady-state dynamics are also analyzed to show the stability of the equilibrium. Empirically, to show the usefulness of the theoretical model, a numerical simulation is performed. By using two readily available housing market data – the expected time on the market and the average number of trades – it is possible to determine the key variables of the model: price, market tightness and matching opportunities for both buyers and sellers. Although the numerical simulation concerns the Italian housing market, the proposed model is generally valid, being empirically applicable to all real estate markets characterized by non-negligible trading frictions. Indeed, the proposed model can be used to compare housing markets with different features (concerning the search and matching process), as well as analyse the same housing market in different time periods (because the efficiency of the search and matching process can change).Findings– Several important results are obtained. First, the price adjustment – i.e. the difference between the actual selling price and the price obtained in an ideal situation of frictionless housing market – is remarkable. This means that the sign and the size of the price adjustment depend on the extent of trading frictions in the housing market. Precisely, the higher the trading frictions on the demand side (more buyers and less sellers), the higher the actual selling price (the price adjustment is positive), whereas the higher the trading frictions on the supply side (less buyers and more sellers), the lower the actual selling price (the price adjustment is negative). Accordingly, the real estate appraisers should assess the trading frictions in the housing market before determining the price adjustment. Second, an increase in the number of trades affects the house price only if the time on the market varies. Also, the higher the variation in the time on the market, the larger the house price adjustment. Indeed, the expected time on the market reflects the opportunities to matching for both parties and thus the trading frictions. If the time on the market increases (decreases), the seller will receive less (more) opportunities to match; thus, the actual selling price will be driven downwards (upwards).Originality/value– As far as the authors are aware, none of the existing works in the search and matching literature has considered how to take advantage of this theoretical approach to estimate the house price in the presence of trading frictions in the housing market. Indeed, the proposed theoretical model may be a useful tool for real estate appraisers, as it is able to derive the trading frictions from the time on the market and the number of trades, thus estimating properly the house price.

This study analyzes the relationship between the housing and stock markets, focusing on housing market bubbles. Stock market dynamics generally have a more significant impact on housing price movements than housing market dynamics have on stock dynamics. However, if housing market information is provided as a signal, housing price movements can predict stock market volatility. Accordingly, we build a machine learning-based early warning system (EWS) for the housing market using a long short-term memory (LSTM) neural network. Applying the generalized supremum augmented Dickey-Fuller test to extract the bubble signal in the housing market, we find that the signal simultaneously detects future changes in the housing market prices and future stock market volatility, and our EWS effectively detects the bubble signal. We confirm that the LSTM approach performs better than other benchmark models, the random forest and support vector machine models.

Discussion

Purpose This study aims to analyze the main factors affecting house prices in various major Moroccan cities. This study uses a hedonic pricing model with robust standard errors to figure out the relationship between the structural, amenity and locational characteristics and house prices. This study draws on data from Avito.ma, a leading Moroccan online real estate platform, filling a major gap in understanding housing markets across Morocco and the wider MENA region while providing valuable insights for policymakers, developers and investors. Design/methodology/approach This study uses an Ordinary Least Squares model with robust standard errors. The dependent variable is the natural logarithm of housing prices. The analysis is based on 1,088 verified housing listings collected from Avito.ma between February and May 2025. The independent variables consist of property size, floor level and a range of amenities, including parking, air conditioning, sports facilities and security. To capture regional differences, city fixed effects were added to the model. This approach makes it possible to eliminate local price dynamics and account for unobserved spatial variations. A Chow-type test was conducted to establish whether the factors influencing housing prices differ from one city to another. Separate regressions were estimated separately for the cities included in the data set, and the results were compared using joint Wald tests. The null hypothesis of equal parameters across cities was rejected at the 5% level, showing that housing price determinants vary significantly between regions. Additional diagnostics confirmed that the model performs reliably and is statistically sound. Findings The findings of this study indicate that property size is the predominant factor influencing home costs, while the advantage of additional space diminishes after a specific threshold is attained. Amenities significantly influence property value; homes equipped with security systems command approximately 7.8% higher prices, while those with parking spaces sell for roughly 5.9% more. Conversely, properties equipped with heating systems or in regions exhibiting indicators of social hardship generally possess lower value. Properties with ocean views command a significant premium of approximately 26%. The inclusion of city fixed effects clarifies the variations in property markets across Morocco. Prices in Rabat and Casablanca are significantly higher compared to neighboring cities, although El Jadida (−26%), Azemmour (−33%) and Kenitra (−11%) exhibit substantial reductions. The findings confirm that Morocco’s urban housing market operates within a polycentric spatial framework instead of a purely monocentric one. Research limitations/implications Although the Web sources provide valuable insights into housing market dynamics in major urban centers, they lack sufficient information about informal settlements and rural areas, leading to selection bias. The four-month observation window also makes it difficult to examine seasonal and cyclical dynamics. The field should move away from static, cross-sectional analyses and toward dynamic frameworks that can show how housing factors change over time in response to changes in the economy, policy and population. The use of a mixed method will help us better understand the factors affecting housing values. Practical implications The findings highlight the need for geographically differentiated housing policies. To promote balanced regional development, policymakers should prioritize increasing infrastructure, safety and urban amenities in undervalued cities, while developers are encouraged to include security and parking facilities as value-enhancing investments. In high-demand areas like Rabat and Casablanca, supply-side initiatives such as inclusionary zoning can help with affordability, whereas periphery cities would benefit more from basic infrastructure and service upgrades. Social implications The recommendations based on the results help make cities more livable and welcoming to everyone. Better neighborhoods and better connections between cities can help people move up in society, lower inequality between areas and encourage balanced growth in cities. Making things safer, easier to get to and less expensive makes not only life better for people who live there but also the community healthier by making living spaces safer, more connected and more cohesive. Originality/value To the best of the authors’ knowledge, this study is one of the first rigorous applications of hedonic pricing model to Morocco’s housing market, using high-frequency internet data. This study contributes to the empirical literature by merging modern amenities, spatial fixed effects and robust estimating methodologies, which improve methodological precision and policy relevance. The findings serve as an analytical foundation for evidence-based urban planning and housing policy in MENA’s growing economies.