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This paper aims to use Markov switching vector auto regression (MSVAR) methods to examine UK house price cycles in UK regions at NUTS1 level. There is extensive literature on UK regional house price dynamics, yet empirical work focusing on the duration and magnitude of regional housing cycles has received little attention. The research findings indicate that the regional structure of UK exhibits that UK house price changes are best described as two large groups of regions with marked differences in the amplitude and duration of the cyclical regimes between the two groups.

MSVAR principal component analysis NUTS1 data are used.

The housing cycles can be divided into two super regions based on magnitude, duration and the way they behave during recession, boom and sluggish periods. A north-south divide, a uniform housing policy and a monetary policy increase the diversion among the regions.

Markov switching needs high-frequency data and long time spans.

Questions a uniform housing policy in a heterogeneous housing market. Questions the impact of monetary policy on a heterogeneous housing market. The way the recovery of the housing market varies among regions depends on regional economic performance, housing market structure and the labour market. House price convergence, beta-convergence.

No such work has been done looking at duration and magnitude of regional housing cycles. A new econometric method was used.

This paper aims to bring together research on housing market area, submarket and household migration into a systems approach that helps us gain a better understanding of the structure and dynamics of a housing market and identify housing problems for a large metropolitan area.

The paper uses a geographic information system (GIS)-based method with simple quantitative techniques, including spatial analysis, location analysis, house price clustering and cross-tabulation. The analysis is based on migration data from the 2011 Census, house price data from the Land Registry in 2011 for Greater Manchester at the ward level and the output areas level.

The results show that different submarkets and housing market areas had different patterns of spatial migration and connections with other areas. Through a systematic analysis of migration and house price in combination, it also found a close connection between destination submarkets and the ages of migrants and identified specific problematic patterns for a large metropolitan area.

The interactions between the owner-occupied sector and the social and private rented sectors are arguably an important omission from the analysis. Also, it is acknowledged that clustering ward units based on price differentials is subject to distortions in terms of specification, size and shape. Moreover, the use of the large samples may result in very small p-values, leading to the problem of the rejection of the predefined hypothesis.

A systematic analysis of migration and house price in combination may be used to gain a better understanding of the housing market dynamics and identify housing problems systematically for a large metropolitan. It may help to identify low-demand areas, high-demand areas and assist planners with decisions in allocating suitable land for new housing constructions.

The GIS-based method introduced in the paper could be considered as an effective approach to provide a better basis for determining policy interventions and public investment designed to allocate land resources effectively and improve transport systems to change existing problematic migration patterns.

This paper fills a gap in the international literature in relation to adopting a systems approach that analyses migration and house price data sets in combination to systematically explore migration patterns and linkages and identify housing problems for a large metropolitan area. This systems approach can be applied in any metropolitan area where migration and house price data are available.

We study research designs where a binary treatment changes discontinuously at the border between administrative units such as states, counties, or municipalities, creating a treated and a control area. This type of geographically discontinuous treatment assignment can be analyzed in a standard regression discontinuity (RD) framework if the exact geographic location of each unit in the dataset is known. Such data, however, is often unavailable due to privacy considerations or measurement limitations. In the absence of geo-referenced individual-level data, two scenarios can arise depending on what kind of geographic information is available. If researchers have information about each observation’s location within aggregate but small geographic units, a modified RD framework can be applied, where the running variable is treated as discrete instead of continuous. If researchers lack this type of information and instead only have access to the location of units within coarse aggregate geographic units that are too large to be considered in an RD framework, the available coarse geographic information can be used to create a band or buffer around the border, only including in the analysis observations that fall within this band. We characterize each scenario, and also discuss several methodological challenges that are common to all research designs based on geographically discontinuous treatment assignments. We illustrate these issues with an original geographic application that studies the effect of introducing copayments for the use of the Children’s Health Insurance Program in the United States, focusing on the border between Illinois and Wisconsin.

The purpose of this paper is to demonstrate how the specification of hedonic pricing models can be improved by using insights generated from qualitative research. In doing so, it seeks to address one of the main problems in the specification of hedonic models, namely that theory yields little guidance in the selection of the characteristics that should be included on the right-hand side.

Building on the behavioural tradition in real estate research, this paper introduces a research approach that integrates insights from qualitative analysis in an econometric model of land values. The empirical segment explores the way in which asking prices of building plots for industrial purposes are determined in The Netherlands. It draws from interviews with municipal land developers, who dominate supply in this market. The information secured during these interviews relates to the characteristics considered important and the kind of information used in the valuation process. Based on these qualitative data, an econometric model is developed and estimated.

The estimation results confirm qualitative evidence that the typical developer considers only a limited number of features of the land in the valuation process and that the primary source of information in setting asking prices relates to the prices charged in neighbouring municipalities.

This paper represents a novel attempt to examine the determination of land and property values by merging qualitative and quantitative, econometric analyses.

Tobler's law of geography states that things that are close to one another tend to be more alike than things that are far apart. In this regard, the spatial pattern of price distribution is defined by the arrangement of individual entities in space and the geographic relationships among them. The purpose of this paper is to provide emerging findings of research analysing the salient factors which impact on the sale price of residential properties using a spatial regression approach.

The research develops and formulates a geographically weighted regression (GWR) model to incorporate residential sales transactions within the Belfast Metropolitan Area over the course of 2010. Transaction data were sourced from the University of Ulster House Price Index survey (2010, Q1‐Q4). The GWR approach was then evaluated relative to a standard hedonic model to determine the spatial heterogeneity of residential property price within the Belfast Metropolitan Area.

This investigation finds that the GWR technique provides increased accuracy in predicting marginal price estimates, in comparison with traditional hedonic modelling, within the Belfast housing market.

This study is one of only a few investigations of spatial house price variation applying the GWR methodology within the confines of a UK housing market. In this respect it enhances applied based knowledge and understanding of geographically weighted regression.

The purpose of this paper is to comment upon the use of hedonic pricing models for the valuation of property. This model can be particularly useful for some housing markets.

This Education Briefing is an explanation of the how hedonic pricing can be useful in looking at the effect of “location” on the house prices within different submarkets using the Italian real estate market as an example.

Although, this case study is relatively straightforward, it shows how the application of the market approach can provide insights in cases where the comparable properties belong to different submarkets with relatively few transactions.

In cases of mass appraisals, hedonic pricing models can provide a broad indication of value across submarkets.

This paper develops a general framework that connects multiple regression analysis, direct comparison model and submarket binary variables.

There is a long-standing debate about the relationship between land title registration and tenure security. Studies in the developing world point to a tenuous link between land registration and stable land tenure. The reason why people continue to register therefore becomes a mystery if tenure security is not entirely assured. This article focuses on the increase in property value as one such factor that induces title registration. Previous studies have quantified the economic impact of title registration on property values. However, the impact varies from city or country to another. The authors seek to investigate the extent of property value increment in Accra attributable to land title registration.

The authors statistically analyzed a data set from two institutions (First National Bank and the Lands Commission) in Ghana using a quantitative technique.

The authors discovered that, holding all other factors constant, the value of the land in Accra increases by 22.6% due to land title registration. This shows that lessees must register to enhance property values, even though the essential due diligence must be done to make sure the acquisition is free from liens and legal disputes.

This article highlights the implication of the findings for land administration as well as the practice of property valuation, development and brokerage in Ghana and Global South more broadly.

This is one of the first studies in Ghana to investigate the specific premium that housing markets put on land title registration.

The housing sector is one of the main sources of economic growth in both developing and developed countries. Although many methods for modeling house prices have been proposed, each has its own limitations. The present paper aims to propose gene expression programming (GEP) as a new approach for prediction of housing price.

This study introduces gene expression programming (GEP) as a new approach for predicting housing price. This is the first time that this metaheuristic method is used in the housing literature.

The housing price model based on the gene expression programming is compared with a least square regression model that is derived from a stepwise process. The results indicate that the GEP‐based model provides superior performance to the traditional regression.

Data used in this study is derived from the Household Income and Expenditure Survey (HIES) in Iran that is conducted by the Statistical Center of Iran (SCI). Housing price model is estimated by administering the questionnaires of this survey in Hamedan Province. To show the applicability of the derived model by GEP technique, it is verified applying parts of the data, namely test data sets that were not included in the modeling process.

The paper aims to estimate the effect of energy efficiency on the Market Value of office buildings and consider whether this effect increases over time.

The authors analyze a dataset of office building valuations from 2009 to 2011, provided by the German Investment Property Database. The authors use hedonic regression models to determine the effect of energy efficiency and energy consumption on Market Values. Using generalized additive models (GAM) for modeling nonlinear covariate effects, the authors control for further building characteristics and location. Due to the small sample size, the authors introduce an innovative econometric approach that mitigates this problem.

Mainly due to the small sample size, and in spite of the newly developed econometric methodology, the authors do not find clear evidence of the relationship between energy efficiency and the Market Value. However, the study nonetheless provides interesting insights into the composition of office building Market Values in Germany.

In addition to the empirical results for the German office market, the main contribution of this paper lies in the econometric methodology. Beside the application of cutting-edge statistical techniques, the authors develop a method for handling datasets, for which the variable of interest is rarely observed, leveraging on the total available data. Thus, the methodology offers promising prospects for future research in similar settings.

The private rental market in Malta has expanded significantly in recent years, but as at 2020, no official rent index is yet published. This paper aims to construct such an index and explores the relative importance of structural, locational and neighbourhood factors to advertised rents.

The authors compile hedonic indices for advertised rents in Malta collected from publicly available sources using webscraping techniques. The database comprises more than 25,000 listings with information on various property attributes. Hedonic regressions are estimated using ordinary least squares and rent indices are computed using three alternative methods: the time dummy method, the rolling time dummy method and the average characteristics method. For the latter, indices are computed using the Laspeyres, Paasche and Fisher methods.

The results from the hedonic indices indicate that the annual growth rate in advertised rents was slowing down during 2019, albeit still remaining relatively high, while in 2020, advertised rents contracted sharply, amplified by the effects of COVID-19. The findings also reveal that advertised rental prices are significantly influenced by various structural, locational and neighbourhood factors.

This paper introduces the first rent index in Malta that will be used to monitor developments in the rental segment of the housing market and for financial stability purposes given the share of buy-to-let properties. It also provides various elasticities on the impact of property attributes on advertised rents in Malta. Finally, the study contributes to the literature on the effect of foreign-born residents on advertised rents.