Housing Market Activity Research Articles

Housing market activity diffusion in England and Wales

<abstract> <p>This paper considers housing activity transmission across space using local indicators of spatial association. Activity does not diffuse like a ripple across England and Wales. Elevated activity appears to be associated with a south east-north west divide. The south east grows more quickly than the north west for a period of time, after which there is a switch in rank order. It is argued that changes in activity is associated with changing credit conditions to which the south east is more sensitive. Furthermore, it is proposed that activity features a type of participant induced on to the market by favourable price changes and credit conditions. It is argued that the reduced activity associated with 'missing mortgage movers' post-2007 reflects the lack of mortgage mover participation, which could have negative consequences for the macroeconomy.</p> </abstract>

Early effects of COVID-19 pandemic-related state policies on housing market activity in the United States

I use daily and weekly data from 100 metropolitan areas in 2020 to investigate the effects of state-level policies to combat the COVID-19 pandemic on various indicators of U.S. housing market activity. Measures of housing market activity include change in new listings, total inventory, newly pending sales, median list price, web traffic to for-sale homes, and average number of days to pending sale status. Using event study and difference-in-differences models, I find that the closure of non-essential businesses in certain states was associated with up to an 11-percentage point decrease in new home listings and a 3.5 percentage point decrease in total inventory relative to the same period in 2019. I also find that school closures may affect some outcomes.

FORECASTING SPATIALLY CORRELATED TARGETS: SIMULTANEOUS PREDICTION OF HOUSING MARKET ACTIVITY ACROSS MULTIPLE AREAS

This study involved the development of an approach to forecast house prices and trading volumes across multiple areas simultaneously. Spatially correlated targets, such as house prices, can be predicted more accurately by leveraging the correlations across adjacent areas. A multi-output recurrent neural network, a deep learning algorithm specifically developed to analyze sequence data, was utilized to forecast the house prices and trading volumes in the four chosen study areas. The forecasting accuracy of future house prices in one of the four geographical areas clearly improved; this area was found to be a price-lagging area, and the forecasting accuracy of this area significantly increased by exploiting the information of a price-leading area. As for the prediction of trading volumes, the difference in performance between the multi-output recurrent neural network and conventional models was very small. The results of this study are expected to promote the use of deep learning to predict the housing market activity through a simultaneous forecasting framework.

The Pearls and Perils of Google Trends: A Housing Market Application.

This study aims at providing insights into the correct usage of Google search data, which are available through Google Trends. The focus is on the effect of sampling errors, which has not received the attention that it deserves. A housing market application is used to demonstrate the effects. For this purpose, the relationship between online search activity for mortgages and real housing market activity is investigated. A simple time series model, which explains transactions by an online mortgage search, is estimated. The results show that the effect of sampling errors is substantial. Thus, although the application of Google Trends data in research remains promising, far more attention should be given to the limitations of these data.

History Dependence in the Housing Market

Using data on the universe of housing transactions in England and Wales over a 20-year period, we document that sale prices and selling propensities are affected by house prices prevailing in the period in which properties were previously bought. Using administrative data on mortgages, we show that cognitive frictions explain most of the history dependence in sale prices, whereas credit frictions are more relevant for selling propensities. We corroborate our analysis with data on online house listings, and we estimate the impact of history dependence on the collapse and slow recovery of housing market activity in the postcrisis period. (JEL E32, R21, R31)

The Regional Spatial Diversity of Housing Prices and Market Activity – Evidence from Poland

The aim of this study is to identify the factors that significantly determine the regional spatial differentiation of housing prices as well as housing market activity in Poland. The present research makes the assumption that average housing prices and market activity (number of transactions) are regionally shaped by economic, social, infrastructural and environmental conditions which can be described as a set of diagnostic features ascribed to a given area, constituting a statistical unit. Furthermore, it is assumed that individual effects may appear, being tied to the idiosyncrasies and significance of the given area. The time horizon of the research is 2008-2018. Based on data sourced from the Central Statistical Office a panel data was prepared for each of 380 Polish districts (poviats). Next, parameters were estimated for a single-factor panel model, as well as a two-factor model in which the constant term is different for different time periods and different units. This resulted in a model encompassing both average price determinants, and individual effects which reflect certain regularities of their spatial distribution. Moreover, the research will result in a set of cartograms made with Geographic Information System tools, depicting the random effects resulting from estimates of panel models using the Nerlove and Swamy-Arora transformations.

Multiple Reference Points and Length of Ownership in the Housing Market

Reference-dependent utility has been shown to have a significant influence on housing market activity. Sellers facing a nominal loss on their home due to market conditions, tend to inflate the asking price and thus the transaction price of their home, indicative of loss aversion. However, the original purchase price of a home may lose influence as a reference-point as the period of ownership increases, while homeowners rely increasingly on other benchmarks such as neighboring homes’ sales prices as reference points. In other words, a homeowner may have multiple reference points in their underlying utility function, whose relative influences shift in importance over time. We use comprehensive administrative data on residential real estate transactions in Singapore’s private housing market to test the influences of original purchase price and recent neighborhood price as reference points over the length of homeownership. Purchase price and neighborhood price are each valid reference points based on established empirical methods in the behavioral and housing economics literature. By testing for their relative influences as a function of length of ownership, we find that prior purchase price is more influential for the first years of ownership, while neighborhood price becomes more influential during the subsequent holding years. Finally, we provide a theoretical framework for understanding the relative influences of multiple reference points over time, which supports the empirical findings.

The consumption response to borrowing constraints in the mortgage market

This paper shows that relaxing borrowing constraints positively affects household consumption in addition to stimulating housing market activity. We focus on the UK Help-to-Buy (HTB) program, which provided a sudden relaxation of the down payment constraint by facilitating home purchases with only a five percent down payment. Our research design exploits geographic variation in exposure to HTB and uses administrative data on mortgages and car sales in combination with household survey data. We estimate that the program increased total home purchases by 11 percent, and the increase was driven almost entirely by first-time and young buyers. Regions that were more exposed to the program experienced a rise in non-durable consumption unrelated to the home and in loan-financed car purchases, in addition to an increase in home-related expenditures. These results are independent of changes in regional house prices. Our findings point to a further link between the housing market and household consumption that does not operate through the home purchase and housing wealth channels.

Spatial Analysis of Housing Prices and Market Activity with the Geographically Weighted Regression

The main part of the study will be to demonstrate that models taking into account spatial heterogeneity (Geographically Weighted Regression and Mixed Geographically Weighted Regression) which reproduce housing market determinants better reflect market relationships than conventional regression models. The spatial heterogeneity of the housing market determinants results in the spatial diversity of the market activity, as well as of real estate prices and values. The main aim of the study was to analyse an effect of these socio-demographic and environmental factors on average housing property prices and on the number of transactions in a spatial approach. In previous research conducted on a national scale, usually all variables were treated in a similar way, i.e., as global or local variables. During the research, an attempt was also made to answer the question of which of the variables adopted for analysis have a local impact on prices and market activity, and which are global. The study was conducted in Poland and used data from the year 2018 on 380 counties (Local Administrative Units). The study showed that determinants both for average prices and for the housing market activity show spatial autocorrelation with high–high and low–low cluster groups. Owing to these models, it was possible to draw specific conclusions on local determinants of flat prices and the market activity in Poland. The study findings have confirmed that they are an extremely effective tool for spatial data analysis.

3D Modeling of Discontinuity in the Spatial Distribution of Apartment Prices Using Voronoi Diagrams

An immanent feature of the housing market is a large spatial dispersion of real estate prices along with their simultaneous high stratification. Application of classic methods of data interpolation results in an excessive simplification of the outcome because of a conversion of the dispersed data sets into areas of spatial continuity by reducing the above-average real estate prices. The main aim of the article was to search for spatial discontinuities of real estate prices’ distribution with 3D modeling using Voronoi diagrams as a method of irregular division of this space. Used methods of geospatial analyses with GIS tools enabled to identify clusters of high housing market activity and to avoid an excessive generalization of data resulting from the reduction of the above-average real estate prices. The research was conducted for over 7000 real estate transactions in years 2010–2017 in Olsztyn, the capital city of Warmia and Mazury in Poland, resulting in a 3D visualization of real estate prices for the chosen market, including the discontinuity in their spatial distribution.

Regional Housing Market Conditions in Spain

Selling homes is not easy. Home sellers usually need to apply a price discount to swiftly close a deal, and more so when housing market activity is low. Using detailed data on home listings and transactions in Spain, we provide unique estimates of the price discount across regional submarkets and time. We document that the price discount is strongly countercylical, as it increases with declining market conditions, and viceversa during upturns. Despite substantial heterogeneity, regional price discounts are synchronized and a single common factor can account for about sixty percent of their variation, thus suggesting the existence of a national housing cycle. Finally, we document that the main factors linked to changes in the price discount are developments in income, population, and interest rates, which are jointly able to explain the bulk of variation in housing market liquidity across regions and time. Besides providing a formal test of the performance of the price spread in gauging housing market liquidity, this study conveys practical information to real estate market participants, policymakers, and financial institutions for which assessing conditions in Spanish housing markets is a central task.

Do factors influencing consumer home-buying attitudes explain output growth?

PurposeThe purpose of this paper is to focus on the relationship between attitudinal data from the long-running Michigan Surveys of Consumers and US real GDP growth. One survey question asks, “Generally speaking, do you think now is a good time or a bad time to buy a house?” with the follow-up question “Why do you say so?” There are several factors for consumers to choose as reasons. Given the strong link between US housing market activity and business cycles, the authors ask whether the responses to the follow-up question explain the behavior of output growth.Design/methodology/approachThe authors employ an augmented autoregressive model to investigate the relationship between output growth and the responses to the follow-up question for 1986–2007 and for 1986–2018, which includes the 2008 financial crisis. The authors follow the general-to-specific approach to obtain the final model estimates for interpretation. For a deeper analysis, the authors estimate the model using the responses of survey participants in the bottom 33 percent, middle 33 percent and upper 33 percent income categories, separately. While avoiding aggregation bias, this approach helps reveal important information embodied in the cross-sectional distribution of the data.FindingsThe follow-up question focuses on such factors as home prices, mortgage rates, houses as a good/bad investment, timing, uncertain future and affordability. The authors find that the majority of these factors chosen as reasons by consumers in the middle and upper 33 percent income categories explain the behavior of output growth. Among the factors chosen as reasons by consumers in the bottom 33 percent income category, only the mortgage rate and uncertain future explain output growth.Originality/valueThis study provides new insights into the usefulness of detailed consumer survey data in explaining the behavior of output growth and further underlines the usefulness of such measures across different income categories for revealing important information contained in the cross-sectional distribution of the data.

Mortgage recourse provisions and housing prices

In light of the large swings in housing prices in the United States in recent years, there has been considerable interest in trying to understand the various factors which led to the boom and bust of the housing market. In this paper, we explore the impact of the legal environment from provisions for mortgage default across U.S. states. To do so, we develop a rigorous framework with microeconomic foundations for financial intermediaries. To be specific, we introduce a housing market and a residential mortgage market in the Diamond and Dybvig framework which emphasizes the role of depository institutions to help depositors manage idiosyncratic liquidity risk. Notably, we think of non-recourse provisions as a legal arrangement to protect risk-averse homeowners from the loss of housing value. While housing demand should be higher in markets where mortgage borrowers have full insurance, lenders also adjust the amount of mortgage credit provided to protect their risk-averse depositors. Thus, a priori, there is not an obvious connection between mortgage recourse provisions and housing prices. To draw further insights into the issue, we proceed to look at empirical evidence on housing prices at the MSA-level using the Case-Shiller Home Price Index. Once one controls for regional level unobservables, the evidence suggests that the demand side factors dominate in which prices are higher in non-recourse states, following the prediction from the model that the demand for mortgages would be higher. We next move to obtain more concrete predictions from our theoretical framework with calibration exercises to study the effects of mortgage recourse. Upon calibrating the model to match some stylized evidence on housing market conditions, the theoretical predictions are consistent with the regression analysis. In this manner, our work sheds numerous insights into the implications of the legal landscape regarding mortgage default for housing market activity.

Housing Market Activity is Associated with Disparities in Urban and Metropolitan Vegetation

In urban areas, the consistent and positive association between vegetation density and household income has been explained historically by either the capitalization of larger lawns and lower housing densities or landscaping and lifestyle districts that convey prestige. Yet cities with shrinking populations and rising land burdens often exhibit high vegetation density in declining neighborhoods. Because the observed associations do not directly address the causal connection between measures of social privilege and vegetation in urban landscapes, it is difficult to understand the forces that maintain them. Here, we compare patterns of household income with new measures derived from housing market data and other parcel-level sources—sale prices, tax foreclosures, new housing construction, demolitions, and the balance of construction and demolition. Our aim is to evaluate whether these spatially, temporally and semantically finer measures of neighborhood social conditions are better predictors of the distribution of urban vegetation. Furthermore, we examine how these relationships differ at two scales: within the City of Detroit and across the Detroit metropolitan area. We demonstrate, first, that linear relationships between income or home values and urban vegetation, though evident at broad metropolitan scales, do not explain recent variations in vegetation density within the City of Detroit. Second, we find that the real estate and demolition records demonstrate a stronger relationship with changes in vegetation density than corresponding changes in US Census measures like income, which suggests they hold at least as much interest for understanding how the relationships between biophysical changes and neighborhood change processes come about.

Demonstrating a New Measure & Index of U.S. Homebuilder Confidence

Our objective in this paper is to apply behavioral economic theory and evidence from market actor perception surveys to the housing industry to demonstrate a new measure of homebuilder confidence. From the literature, we identify the factors that likely influence this confidence. Then, using pilot survey data on new homebuilders, as well as renovation and replacement contractors, we generate a new metric. The results suggest that this measure is associated with measures of housing market activity in the short term. The initial data is limited. Future research should examine the durability of these relationships over time.

Crisis and the Reproduction of Core-Periphery Relations on the Hungarian Housing Market

Housing markets can be understood as indicators of the spatial pattern of capital investment under contemporary financial capitalism. We take this point of entry in order to analyze core-periphery relations around the turning point of the global financial crisis of 2007–2008 on the Hungarian housing market. The instance of crisis sheds light on patterns of homogenization and differentiation; the effects of which can be understood by exploring housing market activity on various scales from the European to the local/regional. We argue that these two patterns of uneven development are inextricably linked and result in deepening sociospatial polarization.

Housing Market Responses to Transaction Taxes: Evidence From Notches and Stimulus in the U.K.

We investigate housing market responses to transaction taxes using administrative data on all property transactions in the U.K. from 2004 to 2012 combined with quasi-experimental variation from tax notches and tax stimulus. We present two main findings. First, transaction taxes are highly distortionary across a range of margins, causing large distortions to the price, volume, and timing of property transactions. Secondly, temporary transaction tax cuts are an enormously effective form of fiscal stimulus. A temporary elimination of a 1% transaction tax increased housing market activity by 20% in the short run (due to both timing and extensive responses) and less than half of the stimulus effect was reversed after the tax was reintroduced (due to re-timing). Because of the complementarities between moving house and consumer spending, these stimulus effects translate into extra spending per dollar of tax cut equal to about 1. We interpret our empirical findings in the context of a housing model with downpayment constraints in which leverage amplifies the effects of transaction taxes.

The impact of monetary policy on housing market activity: An assessment using sign restrictions

Existing research demonstrates that housing, particularly residential investment, plays an important role in the transmission of monetary policy shocks to the overall economy. With this in mind, this paper investigates the relationship between monetary policy and housing market activity using a relatively new method for identifying monetary shocks. More specifically, a monetary policy shock is identified by explicitly imposing sign restrictions on impulse response vectors. The extra information from sign restrictions is important for new insights regarding the transmission of monetary policy to the housing sector – notably, the results indicate that residential investment is less sensitive to a contractionary shock than standard estimates with recursive restrictions. Given that the response of the housing sector using sign restrictions is smaller than other work using standard identification methods, the work indicates that further research is needed to examine whether other sectors of the economy may be less sensitive to monetary policy than previously thought.

Informal Housing in the United States

AbstractResearch on informal housing tends to focus overwhelmingly on less developed countries, downplaying or ignoring entirely the presence of informality in United States housing markets. In actuality, a longstanding and widespread tradition of informal housing exists in the United States but is typically disregarded by scholars. In this article we draw on three definitions of informality—as non‐compliant, non‐enforced, or deregulated economic activity—to characterize examples of informality in US housing markets, focusing in particular on five institutions that govern housing market activity in this country: property rights law, property transfer law, land‐use and zoning, subdivision regulations, and building codes. The cases presented here challenge the notion that informality is absent from US housing markets and highlight the unique nature of informal housing, US style—namely, that informal housing in the US is geographically uneven, largely hidden and typically interwoven within formal markets. We conclude with a discussion of how research on informal housing in the US can inform research in the global South.

Social Networks and Housing Markets

We document that the recent house price experiences within an individual’s social network affect her perceptions of the attractiveness of property investments, and through this channel have large effects on her housing market activity. Our data combine anonymized social network information from Facebook with housing transaction data and a survey. We first show that in the survey, individuals whose geographically-distant friends experienced larger recent house price increases consider local property a more attractive investment, with bigger effects for individuals who regularly discuss such investments with their friends. Based on these findings, we introduce a new methodology to document large effects of housing market expectations on individual housing investment decisions and aggregate housing market outcomes. Our approach exploits plausibly-exogenous variation in the recent house price experiences of individuals’ geographically-distant friends as shifters of those individuals’ local housing market expectations. Individuals whose friends experienced a 5 percentage points larger house price increase over the previous 24 months (i) are 3.1 percentage points more likely to transition from renting to owning over a two-year period, (ii) buy a 1.7 percent larger house, (iii) pay 3.3 percent more for a given house, and (iv) make a 7% larger downpayment. Similarly, when homeowners’ friends experience less positive house price changes, these homeowners are more likely to become renters, and more likely to sell their property at a lower price. We also find that when individuals observe a higher dispersion of house price experiences across their friends, this has a negative effect on their housing investments. Finally, we show that these individual-level responses aggregate up to affect county-level house prices and trading volume. Our findings suggest that the house price experiences of geographically-distant friends might provide a valid instrument for local house price growth.