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This paper aims to study the phenomenon known as “house price dispersion”, one of the most important distinctive features of housing markets. House price dispersion refers to the phenomenon of selling two houses with very similar attributes and in near locations at the same time but at very different prices.

This theoretical paper makes use of a search and matching model of the housing market. The search and matching models are the benchmark models of the “matching” markets, such as the labour market and the housing market, where trade is a decentralised, uncoordinated and time-consuming economic activity.

Unlike the previous related literature that attributes to the heterogeneity of buyers and sellers a significant part of the price volatility, in this paper, the house price dispersion depends on the housing tenure status of home-seekers in the house search process. Indeed, in the presence of different housing tenure status of home-seekers, the house search process leads to different types of matching. In turn, this implies different surpluses (the sum of the net gains of the parties involved in the trade), and eventually, different surpluses produce different prices of equilibrium.

An interesting research agenda for future works would be an extension of the model to study the effect of “online housing search” on the house search and matching process, and thus, on the house price dispersion.

The main practical implication of this work is that the house price dispersion is an inherent phenomenon in the house search and matching process.

None of the existing and related works of research have considered how to take advantage of the search and matching approach to deal with the phenomenon known as “house price dispersion”, without relying on the ex ante heterogeneity of the parties but looking at the “core” of the house search and matching process.

The purpose of this paper is to renew a research agenda considering the impact that information providers’ processes are having on the housing market; in particular to develop a research agenda around the role of the Internet in shaping households’ perceptions of the spatial nature of housing markets.

This paper reviews the existing literature. It uses preliminary extensive survey findings about the role of the Internet in housing search to hypothesise ways in which households may be affected by this transition.

Not applicable – other than evidence for the growth in the importance of the Internet in shaping households’ housing search.

First, the academy needs to readdress the theory surrounding information acquisition and use insights from economics, sociology and psychology to understand these processes. Second, local authorities and academics should analyse the impact of Internet use on housing market boundaries (and the profound subsequent impact on policy traction). Third, estate agents should reconsider the role of the Internet in shaping housing markets and provide a critical response to the large property search engines.

This paper reviews the literature and explores the necessity of a renewed interest in research on the role of information sources in framing and constraining housing search behaviour.

With access to the large-scale search data from Rightmove plc, the paper firstly indicated the possibility of using user-generated data from online property portals to predict housing market activities and secondly embraced a GIS approach to explore what people search for housing and what they chose and investigated the issue of mismatch between search patterns and revealed patterns. Based on the analysis, the paper contributes a visual GIS-based approach which may help planners and designers to make more informed decisions related to new housing supply, particularly where to build, what to build and how many to build.

The paper used the 2013 housing search data from Rightmove and the 2013 price data from Land Registry with transactions made after the search period and embraced a GIS approach to explore the potential housing demand patterns and the mismatch between searches and sales. In the analysis, the paper employed the K-means approach to group prices into five levels and used GIS software to draw maps based on these price levels. The paper also employed a simple analysis of linear regression based on the coefficient of determination to investigate the relationship between online property views and values of house sales.

The result indicated the strong relationship between online property views and the values of house sales, implying the possibility of using search data from online property portals to predict housing market activities. It then explore the spatial housing demand patterns based on searches and showed a mismatch between the spatial patterns of housing search and actual moves across submarkets. The findings may not be very surprising but the main objective of the paper is to open up a potentially useful methodological approach which could be extended in future research.

It is important to identify search patterns from people who search with the intention to buy houses and from people who search with no intention to purchase properties. Rightmove data do not adequately represent housing search activity, and therefore more attention should be paid to this issue. The analysis of housing search helps us have a better understanding of households' preferences to better estimate housing demand and develop search-based prediction models. It also helps us identify spatial and structural submarkets and examine the mismatches between current housing stock and housing demand in submarkets.

The GIS approach in this paper may help planners and designers better allocate land resources for new housing supply based on households' spatial and structural preferences by identifying high and low demand areas with high searches relative to low housing stocks. Furthermore, the analysis of housing search patterns helps identify areas with latent demand, and when combined with the analysis of transaction patterns, it is possible to realise the areas with a lack of housing supply relative to excess demand or a lack of latent demand relative to the housing stock.

The paper proves the usefulness of a GIS approach to investigate households' preferences and aspirations through search data from online property portals. The contribution of the paper is the visual GIS-based approach, and based on this approach the paper fills the international knowledge gap in exploring effective approaches to analysing user-generated search data and market outcome data in combination.

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.

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).

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).

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.

The paper employs a unique online user-generated housing search dataset and introduces a novel measure for housing demand, namely “contacts per listing” as explained by hedonic, geographic and socioeconomic variables.

The authors explore housing demand by employing an extensive Internet search dataset from a German housing market platform. The authors apply state-of-the-art artificial intelligence, the eXtreme Gradient Boosting, to quantify factors that lead an apartment to be in demand.

The authors compare the results to alternative parametric models and find evidence of the superiority of the nonparametric model. The authors use eXplainable artificial intelligence (XAI) techniques to show economic meanings and inferences of the results. The results suggest that hedonic, socioeconomic and spatial aspects influence search intensity. The authors further find differences in temporal dynamics and geographical variations.

To the best of the authors’ knowledge, it is the first study of its kind. The statistical model of housing search draws on insights from decision theory, AI and qualitative studies on housing search. The econometric approach employed is new as it considers standard regression models and an eXtreme Gradient Boosting (XGB or XGBoost) approach followed by a model-agnostic interpretation of the underlying effects.

The application of “Google” econometrics (Geco) has evolved rapidly in recent years and can be applied in various fields of research. Based on accepted theories in existing economic literature, this paper seeks to contribute to the innovative use of research on Google search query data to provide a new innovative to property research.

In this study, existing data from Google Insights for Search (GI4S) is extended into a new potential source of consumer sentiment data based on visits to a commonly‐used UK online real‐estate agent platform (Rightmove.co.uk). In order to contribute to knowledge about the use of Geco's black box, namely the unknown sampling population and the specific search queries influencing the variables, the GI4S series are compared to direct web navigation.

The main finding from this study is that GI4S data produce immediate real‐time results with a high level of reliability in explaining the future volume of transactions and house prices in comparison to the direct website data. Furthermore, the results reveal that the number of visits to Rightmove.co.uk is driven by GI4S data and vice versa, and indeed without a contemporaneous relationship.

This study contributes to the new emerging and innovative field of research involving search engine data. It also contributes to the knowledge base about the increasing use of online consumer data in economic research in property markets.

This paper aims to explain the main empirical facts of housing markets, notably the trade-off between housing price and time-on-the-market, the positive correlation between housing price and the number of contracts traded during a given period (i.e. the trading volume) and the existence of price dispersion.

This theoretical paper makes use of a search and matching model. Search and matching, indeed, are two fundamental characteristics of the trading process in the housing market, and, thus, the search-and-matching models have become the new economic approach to the analysis of real estate markets.

This paper shows that a slightly modified version of the baseline search and matching model à la Mortensen-Pissarides can explain the main empirical facts of housing markets. There are two key mechanisms that allow to achieve this notable goal: a simple formalisation of the (reasonable) assumption that buyers today are potential sellers tomorrow (and vice versa); and the direct relationship between market tightness and house price, derived by the standard matching model and underestimated by the related literature.

The developed theoretical model only studies the equilibrium conditions. Indeed, it would be interesting to also study the disequilibrium in housing markets.

The explanation of the main empirical facts of housing markets is embodied in the same and relatively simple theoretical model.

In addition to the explanation of the main empirical facts of housing markets, the developed theoretical model can generate an upward sloping Beveridge curve in the housing market (the positive relation between home-seekers and vacant houses). Instead, according to a recent criticism in the related literature, a model à la Mortensen-Pissarides inherently generates a (empirically unrealistic) downward sloping Beveridge curve.

This paper aims to study the relationship between the rental and selling prices, a very important topic that forms the fundamentals of real estate markets.

This theoretical paper makes use of a search and matching model of the housing market. The search and matching models are the benchmark models of the “matching” markets, such as the labour market and the housing market, where trade is a decentralised, uncoordinated and time-consuming economic activity.

Unlike the previous related literature, where this relation is usually analysed in the context of the present value equation, this paper shows the existence of a “dual” relation between rental and selling prices as follows: one in the homeownership market and another one in the rental market. This “dual” relation connects the rental and homeownership markets and allows to get equilibrium in both markets with positive house prices.

Several topics could be deepened for making the paper richer and more interesting, although at the cost of much more mathematics. First of all, the introduction of specific functional forms for both the rent function and the sale price function, so as to calculate both the elasticity of rent with respect to sale price and the elasticity of sale price with respect to rent. In this way, it would be possible to understand how each market (rental and homeownership) reacts to shock and policies that affect the other market.

In general, this framework could help policymakers to design housing policy reforms that take into consideration the effects on both markets. Indeed, some policies could have positive effects on rental markets but perverse effects on homeownership markets and vice versa.

None of the existing and related works of research have considered how to take advantage of the search and matching approach to derive both a “rent function” and a “sale price function” that connect closely the rental and homeownership markets.

This study aims to investigate the determinants of homeowners’ planned holding periods. Real estate market is known for displaying buying and selling behavior that does not conform to traditional economic theories such as rational expectation or expected utility. Mounting evidence of anomalous observations appear to be supported by other theories, such as prospect theory, which in particular helps explain the disposition effect – sellers are too quick to sell when prices are climbing and hold on to properties longer when prices are plummeting. While this evidence is widely documented in housing studies based on data on realized holding periods (i.e. ex post), this study explores factors that may motivate homeowners to alter their expected holding horizons (i.e. ex ante) to form new preferred holding periods that may be shorter or longer than those planned during house search.

The empirical study uses data collected from two cross-section surveys of recent homebuyers in rising and declining housing markets in Southern California in 2004-2005 and 2007-2008, respectively.

The empirical results demonstrate that in addition to the financial characteristics of the recent homebuyer, the characteristics of the buying experience – non-monetary, such as the realized search duration, and monetary, such as perception of negative or positive premium paid for the house relative to its market value – have a statistically significant effect on the holding horizon revision. The data strongly indicate that the perception of having overpaid increases the likelihood of upward revision of the original holding horizon. This effect is stronger in the declining than in the rising market – a crucial finding that mirrors the disposition effect.

This study sheds new light on what may contribute to the disposition effect in housing markets that has not yet been investigated in past literature. The novel approach here is to look at how different house price environments may affect homeowners’ holding periods ex ante when they begin, rather than ex post when already realized.

This study aims to explore the relationship between house prices and time-on-market (TOM) in Silicon Valley. Previous findings have been inconclusive due to variations in property characteristics. This paper highlights the discrepancy between listing and selling prices and identifies differences among housing types such as condominiums, detached houses and townhouses based on housing orientations and customer groups. Additionally, this study considers the impact of the COVID-19 pandemic and the Fed’s interest rate policies on the housing market.

The authors analyze 63,853 transactions from the Bay East Board of Realtors’ Multiple Listing Service during 2018 to 2022. The study uses a multiple-stage methodology, including a nonlinear hedonic pricing model, search theory and two-stage least squares method to address concerns relating to endogeneity.

The Silicon Valley housing market shows resilience, with low-end properties giving buyers more bargaining power without significant price drops. High-end properties, on the other hand, attract more attention over time, leading to aggressive bidding and higher final sale prices. The pandemic, despite reducing housing supply, did not dampen demand, leading to price surges. Post-COVID, price correlations with TOM changed, indicating a more cautious buyer approach toward high premiums. The Fed’s stringent monetary policies post-2022 intensified these effects, with longer listing times leading to greater price disparities due to financial pressures on buyers and shifting dynamics in buyer interest.

Results reveal a nonlinear positive correlation between TOM and the price formation process, indicating that the longer a listed property is on the market, the greater the price changes. For low-end properties, TOM becomes significantly negative, while for high-end properties, the coefficient becomes significantly positive, with effects and magnitudes varying by type of dwelling. Moreover, external environmental factors, especially those leading to financial strain, can significantly impact the housing market.

The experience of Silicon Valley is valuable for cities using it as a development model. The demand for talent in the tech industry will stimulate the housing market, especially as the housing supply will not improve in the short term. It is important for government entities to plan for this proactively.