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The purpose of this paper is to examine whether the house prices in Finland share financial characteristics with assets such as stocks. The studied regions are 15 main regions in Finland over the period of 1988:Q1-2018:Q4. These regions are divided geographically into 45 cities and sub-areas according to their postcode numbers. The studied type of dwellings is apartments (block of flats) divided into one-room, two rooms and more than three rooms apartment types.

Both Ljung–Box and Lagrange multiplier tests are used to test for clustering effects (autoregressive conditional heteroscedasticity effects). For cities and sub-areas with significant clustering effects, the generalized autoregressive conditional heteroscedasticity (GARCH)-in-mean model is used to determine the potential impact that the conditional variance may have on returns. Moreover, the exponential GARCH model is used to examine the possibility of asymmetric effects of shocks on house price volatility. For each apartment type, individual models are estimated; enabling different house price dynamics, and variation of signs and magnitude of different effects across cities and sub-areas.

Results reveal that clustering effects exist in over half of the cities and sub-areas in all studied types of apartments. Moreover, mixed results on the sign of the significant risk-return relationship are observed across cities and sub-areas in all three apartment types. Furthermore, the evidence of the asymmetric impact of shocks on housing volatility is noted in almost all the cities and sub-areas housing markets. These studied volatility properties are further found to differ across cities and sub-areas, and by apartment types.

The existence of these volatility patterns has essential implications, such as investment decision-making and portfolio management. The study outcomes will be used in a forecasting procedure of the volatility dynamics of the studied types of dwellings. The quality of the data limits the analysis and the results of the study.

To the best of the author’s knowledge, this is the first study that evaluates the volatility of the Finnish housing market in general, and by using data on both municipal and geographical level, particularly.

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 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.

The purpose of this paper is to explore the role of the housing market in the monetary policy transmission to consumption among euro area member states. It has been argued that the housing market in one country is then important when its mortgage market is well developed. The countries in the euro area follow unitary monetary policy; however, their housing and mortgage markets show some heterogeneity, which may lead to different policy effects on aggregate consumption through the housing market.

The housing market can act as a channel of monetary policy shocks to household consumption through changes in house prices and residential investment – the housing market channel. The authors estimate vector autoregressive models for each country and conduct a counterfactual analysis to disentangle the housing market channel and assess its importance across the euro area member states.

The authors find little evidence for heterogeneity of the monetary policy transmission through house prices across the euro area countries. Housing market variations in the euro area seem to be better captured by changes in residential investment rather than by changes in house prices. As a result, the authors do not find significantly large house price channels. For some of the countries however, they observe a monetary policy channel through residential investment. The existence of a housing channel may depend on institutional features of both the labour market or with institutional factors capturing the degree of household debt as is the loan-to-value ratio.

The study contributes to the existing literature by assessing whether a unitary monetary policy has a different impact on consumption across the euro area countries through their housing and mortgage markets. The authors disentangle monetary-policy-induced effects on consumption associated with variations on the housing markets due to either house price variations or residential investment changes. The authors show that the housing market can play a role in the monetary transmission mechanism even in countries with less developed mortgage markets through variations in residential investment.

In this paper, we identify the relationship between (local) housing supply and (local) house price developments, especially in The Netherlands.

We measure the influence of new building on house prices by comparing areas designated for concentrated new building (main Dutch cities) with areas where no large housing projects are developed. On the basis of classical economic theory, if the housing market is functioning as it should, then supply will soon respond to a shock in demand and restore stability in house prices.

For the main Dutch cities, we found that an increase in supply triggers a fall in prices. In other areas the correlation coefficients are more or less zero, which can lead us to conclude that the expansion of the housing stock is market‐compliant.

The housing market is not functioning, as it should: new supplies depend on the complex decisions of the suppliers, thus making it difficult to express statistically the causality between the house price developments and the new supplies.

Most studies suggest that macro data are unable to measure the true dependency between the house prices and the new building and claim at the same time that micro data sets are incomplete. Also our research was hampered by a shortage of usable data.

New building can push up the value of the surrounding housing because it is associated with a qualitatively better housing stock. We conclude that in regions where new building has been concentrated in designated areas, the relationship between housing production and price development is inverse.

The purpose of this paper is to address both the measurement of affordability and variations in affordability between local housing market areas (HMAs).

The practical data issues that arise from measuring local affordability are reviewed by reference to studies in the UK. The paper argues that local measures should relate to a functional geography of HMAs rather than simply local authority boundaries. This approach is shown to be more theoretically sound but faces data constraints. An empirical case study of the North West of England then follows as a demonstration based on a tiered geography of HMAs. It addresses the constraints on local income data by measuring affordability by reference to a particular household type and associated income.

Local UK affordability indicators are shown to be primarily about access to home ownership rather than a wider view of local house price structures on affordability. The paper also draws out the importance of affordability measures linked to functional market areas. The results of the analysis presented highlight that there are local differences in house price structures and hence associated differential affordability of house types between local HMAs.

This is the first study that examines affordability at the local level based on functional areas rather than local authority administrative boundaries. This approach gives a truer picture of the variability in local affordability. The applied analysis tackles the data constraints of functional areas and has the potential to be adapted and extended.

Over a long period, even low inflation has an impact on portfolio value and households’ purchasing power. In such a context, inflation hedging should remain an important issue for investors. In particular, long-term investors, who are concerned with the protection of their wealth, seek to hold effective hedging assets. This study aims to demonstrate that residential assets in “Grand Paris” are a hedge against inflation and particularly against its unexpected component.

In this study, the physical residential markets in 127 communes in Paris and the Parisian first-ring suburbs are considered as potential asset classes. We simplified the analysis by clustering the 127 communes into five homogenous groups using ascending hierarchical classification (AHC). Then, we test the hedging ability of these groups within a mixed asset portfolios using both correlation and regression analysis.

This paper presents an analysis of the “Grand Paris” housing market and its inflation hedging ability with comparison to other financial asset classes. Results show that the five housing groups act as a highly positive hedge against unexpected inflation. Furthermore, cash and bonds seem to provide, respectively, a partial and an over hedge against unexpected inflation. Stocks act as a perverse hedge against unexpected inflation and provide no significant hedge against expected inflation. Also, indirect listed real estate demonstrates little correlation with inflation, which makes us reject its hedging ability contrary to physical residential real estate.

The inflation topic: although several researches exist that question the hedging property of real estate, very few concentrate on physical residential assets and to the best of the authors’ knowledge, this study is the only one that targets the “Grand Paris” area. Residential assets of the “Grand Paris” communes are confirmed to be a hedge against inflation and particularly against its unexpected component thanks to its capital appreciation rather than income one. Also, we show that the listed real estate in France (Sociétés d’Investissement Immobilier Cotée) does not provide the same hedging properties contrary to the US real estate investment trusts (REITs) who demonstrate this ability. Listed real estate could thus not be used interchangeably with housing to protect from inflation in the French market.

Protection of investors against inflation and in particular in the face of its return to France in 2022. Reassuring promoters and investors of the interest of residential investment projects in “Greater Paris” and of the potential that this holds.

Inflation takes a chunk out of the purchasing power of money and thereby erodes the real value of people’s finance. Investors and households who seek protection from inflation erosion should invest in direct housing, and in particular within areas that are experiencing an effective metropolization process.

The originality of the study is precisely relative to the geographical area studied. The latter has experienced favorable economic conditions for several years and offers interesting fundamentals to explore and exploit in investment strategies that prove capable of protecting against imminent inflation. The database is specific to this project and has been built through the compilation of several sources and with the support of BNP Paribas Real Estate.

This paper aims to offer an extensive empirical case study analysis by investigating housing affordability in Turkey as a whole, and in Istanbul, Ankara and Izmir over the period of 2006 and 2017 and its sub-periods.

This paper develops a theoretically informed model to assess affordability using complementary methodologies in quantitative analysis. This study seeks to help outline the nature of the problem in aggregate level and in the cities; it also seeks to offer lessons about how to address measurement and modelling challenges in emergent market contexts by constructing aggregate-/city-level housing cost-to-income (HCI) ratio, adjusted HCI (AHCI) ratio, housing affordability index (HAI) and effective HAI sensitive to multiple calculation methodologies and alternative data set involving income distribution and poverty tranches.

HCI, AHCI, HAI and EHAI models generally suggest the parallel results: housing is not affordable in Turkey and in Istanbul, Ankara and Izmir except for the highest income groups. The evidence implies that besides macroeconomic instabilities, distorted interest rates and short average mortgage maturity, poverty and unequal income/wealth distributions are the main reasons of the Turkish housing affordability crisis specifically heightened in metropolitan areas such as in Istanbul.

The evidence provides an insight on housing affordability problems in Turkey. However, small sample size and short observation period create a limit for generalisation of the findings. Further analysis would be required to illustrate how housing affordability changes in different cities of Turkey in a longer period.

By using empirical approaches, this paper helps to understand how serious housing affordability problems of Turkey in aggregate and urban levels. This evidence helps to explain declining ownership ratio in low-income groups and in urban areas. Reliable explanations on existing housing crisis of Turkey also help to develop affordable housing policies.

Declining housing affordability and homeownership ratio may translate as the rising housing inequality and insecurity among Turkish households. Moreover, better affordability values of higher income groups suggest that existing inequality, economic/social segmentation, and hence social tension between high and low income groups, may further increase. In this respect, the authors suggest socially important policies such as reducing income/wealth inequalities and increasing affordable housing supply.

This study offers a detailed empirical case study analysis that can be used as an exemplar of how to overcome data constraints in other evolving housing market contexts. This study sets out an approach overcoming the challenges of measurement. This study also combines existing methodological approaches with the modified variables to provide a more realistic aggregate-/urban-level housing affordability picture. The authors calculated some parts of housing affordability ratio and index series using discretionary income, minimum wage and effective minimum wage to show the variations of different measurement approaches. Some constructed series are also sensitive to income distribution and poverty thresholds. Collectively, this empirical approach, developed by using emerging market data, provides a contribution to the literature.

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 article proposes a schematic model of how community safety is related to changes in local housing markets ‐ particularly those characterised by low demand and market failure. The model is presented as an example of strategic planning for ‘joined‐up’ working in civil and neighbourhood renewal.