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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.

This paper provides household lifetime inequality indexes derived from representative U.S. labor market data. We obtain this result by using estimates of the household search model proposed by Flabbi and Mabli (2012). Inequality indexes computed on the benchmark model shows that inequality in utility values is substantially different from inequality in earnings and wages and that inequality at the cross-sectional level is significantly different from inequality at the lifetime level. Both results deliver original policy implications that would have not been captured without using our approach. In particular, we find that a counterfactual policy experiment consisting in a mean-preserving spread of the wage offers distributions increases lifetime inequality in wages and earnings but not in utility. When comparing inequality at the individual level between men and women, we find inequality in wages and earnings to be higher for husbands than wives but inequality in utility to be higher for wives. A counterfactual decomposition shows that the job offers parameters are the main source of the gender differential.

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.

Previous research has rarely considered purchase acceleration as a measure of consumer deal‐proneness. Also recent studies have found that the effect of demographics either on brand choice or purchase timing is marginal. Examines the effects of demographics on household propensity to accelerate. Selects demographic variables are selected based on theoretical arguments and the results show that their effect is significant. The study found the effects of female head employment status to be the most persistent, followed by income and household size effects. Also found demographics were also found to discriminate between deal‐only and price sensitive households, a difference observed in previous studies.

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 examine the job search probability in welfare receipt over time for men and women in different household constellations, because it is a major concern in welfare states that long-term receipt is driven by recipients’ low job search activity.

The paper analyses the likelihood to search for a job for a sample of unemployed recipients of means-tested welfare benefits in Germany. Data basis is the panel study “Labour Market and Social Security” (PASS), and growth curve models are applied in this study.

Job search probability differs by household constellation and gender directly after the onset of welfare receipt; differences are less distinct for changes in job search probability over time. Only welfare recipients without children show a pronounced decline in search probability.

There is no evidence that welfare recipients’ overall cease to search for a job by and by. Financial incentives alone cannot stimulate the job search of welfare recipients, when the diverse motives of male and female recipients in various household constellations are not considered as well.

This paper is the first to study the long-term development in the job search probability and gender differences by household constellations.

This paper aims to analyze the question of how household indebtedness impacts households’ incentives to search for and accept work after displacement.

To analyze the relationship between household indebtedness and unemployment duration, this paper applies standard proportional hazard models. For data, this paper relies on the longitudinal US National Survey of Income and Program Participation (SIPP), covering the period between 2008 and 2012.

The findings show that a 10% increase in household debt increases the likelihood (hazard) of leaving unemployment by 0.2%–0.4% points. Independent of measuring a household's indebtedness and in light of a series of robustness tests, the results indicate that the pressure of servicing an existing debt burden forces individuals to return to work.

From a policy perspective, the research findings support the notion that household indebtedness plays an important mediating role for labor market outcomes through influencing households’ incentives to return to work after displacement. This finding has important implications for the design of effective policy responses to mass layoffs during the current pandemic.

A key innovation of the research is that we can show that household indebtedness impacts the labor supply side. From a macroeconomic perspective, this insight is important in better understanding the role of increased indebtedness (and financialization) in amplifying aggregate macroeconomic dynamics.

This paper aims to investigate the role of store brands as a time- and money-saving heuristic in the context of an omnipresent store brand hierarchy. Drawing on the work of Tversky and Kahneman (1982), it proposes that the store brand hierarchy is characterised by many of the traits of frequently used heuristics employed by grocery shoppers.

Based on Chaiken’s (1980) model of information processing and Stigler’s (1961) perspective on the economics of information search, the study deductively establishes a model of store brand proneness to reveal the role of store brands as time- and money-saving heuristic. The model is tested on a sample of 535 US households using structural equation modelling and subsequent multigroup analysis based on two subsamples of households experiencing high financial pressure but who differ in terms of time pressure.

The findings provide strong support for store brands as a time- and money-saving heuristic and as a substitute for price search among households experiencing financial and time pressures.

The main limitation is that the study is based on a sample of households located in one region of the US market.

Retailers need to be aware that any extension of the store brand portfolio beyond the traditional multi-tiered price/quality hierarchy risks undermining what has emerged to be a valuable heuristic used by certain shoppers.

This study extends our understanding of the role of store brands in the marketplace by going beyond their conceptualisation as a competitive device used by retailers to instead position them as a decision-making tool used by consumers. It also deepens our understanding of the boundary between rational search activities and the transition to the use of frequently flawed heuristics within the shopping process.

The purpose of this paper is to explore the impact of using computers to obtain information on the farm household’s production and consumption based on a field survey of farm households in the northern China.

The most important methods applied are instrumental variable (IV) method and propensity score matching (PSM) method. Estimators of IV, PSM and nearest neighborhood matching approaches are considered together to check the robustness of empirical results.

This paper careful impact evaluation results suggest that the use of computer not only improves the size of arable land rented in but also reduces family labor input intensity and the probability of selling agricultural outputs at farm-gate markets. Moreover, it also stimulates transportation, garment, housing and insurance expenditures per capita.

The database of this research comprises cross-section data, which does not support a cross-time comparison.

These results imply that it is vital to expand the coverage of computer use in rural areas. This may suggest that the importance of improving computer access is crucial for stimulating rural consumption increase. Furthermore, the need for the expansion of internet network coverage in western areas is also of importance.

First, the authors directly estimate computer usage impacts on a broader range of production and consumption indicators by including land-relative investments, variable investments, labor input and household’s expenditure and provide rigorous impact evaluations on the impact of access to computer. Second, the authors use IV and PSM methods to correct self-selection bias, going beyond the single equation approach in other studies. This enables us to identify the causal relationship between computer usage and farmer’s production and consumption decisions.