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This paper aims to present the dynamics of housing prices in Italian cities based on unpublished data with regional details from the late 1960s, half-yearly base, for all main Italian cities measuring the average prices for three city dimensions: city centre, sub-centres and outskirts or suburbs. It estimates the Italian long-term house price index, city based in real terms, and shows a combination of methods to deal with large time-series data.

This paper builds long-term cycles based on the city (real) data by estimating the common components of cointegrated time series and extracting the unobservable signals to build real house price index for sub-regions in Italy. Three different econometric methodologies are used: Johansen cointegration test and VAR models to identify the long-term pattern of prices at the estimated aggregate level; principal components to obtain the common (permanent and transitory) components; and signal extraction in ARIMA time series–model-based approach method to extract the unobserved time signals.

Results show three long-term cycle-trends during the period and identify several one-direction causal non-permanent relationships among house prices from different Italian areas. There is no evidence of convergence among regional’s house prices suggesting that the Italian housing prices converge inside the local market with only short diffusion effects at larger regional level.

Data are measured as the average price in squared meters, and the resulting index is not quality controlled.

The long-term trends on housing prices serve to implement further research and know deeply the evolution of Italian housing prices.

This paper contains new and unknown information about the evolution of housing prices in Italian regions and cities.

Large numbers of Northern European retirees have migrated to Southern European countries. A relevant part of this migration is not driven by work purposes but rather the desire to establish residence in a warmer country. These migrants come from different countries and exhibit diverse socioeconomic characteristics and preferences, including varying income levels, housing tastes and cultural habits, which could potentially influence the housing market in their host countries. This paper aims to examine the permanent impact of retiree migrant flows on house prices in Alicante, Spain, from 1988 to 2019, explicitly considering the impact related to the country of origin.

This paper examines the permanent impact of retiree migrant flows on house prices in Alicante, Spain, from 1988 to 2019, explicitly considering the impact related to the country of origin using panel cointegration – Dynamic Ordinary Least Squared (DOLS) models.

Results indicate that the long-term relationship captures the entire effect on house price change and that prices react immediately to the immigrants' presence with permanent effects. The results also suggest that the strong retiree migration flow created a shock in the housing market with different effects on house prices related to the immigrants' country of origin. The model identifies that when income growth in the origin country is slower than in Spain it has a major impact on house prices. When purchasing capacity is larger in Alicante than in the origin country it exerts a stronger effect on housing prices. Retiree migration flow has permanent effect on housing market prices.

Results indicate several ways to act on social and housing policies in specific cities in Alicante province, as well as in the origin countries, to alleviate potential disadvantages faced by expatriate retirees.

This paper finds evidence of the specific impact of international retiree migrants on the hosting housing market. This study is the first paper that can estimate the specific effect on housing prices from a flow of retiree migrants by country of origin.

This paper focusses on analysing the impact of crime on the housing market in Los Angeles (LA) County. By looking at different types of crime instead of general crime measures and controlling by spatial dimension of prices and crime as well as endogeneity, a model is developed that allows for the understanding of how a specific crime impacts the housing market transaction price. To perform the analysis, the paper merges different data sets (crime, housing transaction and census data) and then computes the distances to crucial transport modes to control the accessibility features affecting housing prices. The latter allows estimating the association of housing prices and crime in the distance and estimating the impact on housing depending on it.

This paper focusses on the following crimes: aggravated assault, burglary (property crime), narcotics, non-aggravated assault and vandalism. The paper shows firstly how incidents of reported crime are distributed across space and how they are related to each other – thus highlighting crime models with spatial influences. Secondly, the research utilises instrumental variables within the methodology to estimate house prices using spatial analysis techniques while controlling for endogeneity. Thirdly, it estimates the direct impact of crime on house prices and explores the impact of housing and neighbourhood features.

Results suggest that house transaction prices and crime are closely correlated in two senses. Housing prices are endogenously negatively associated with the levels of narcotics and aggravated assaults. For narcotics, the impact of distance is shorter (1,000 m). However, for burglary, vandalism and non-aggravated assaults, the price reaction suggests a positive association: the further away the crime occurs, the higher the prices. The paper also shows the large spatial association of different crimes suggesting that they occur together and that their accumulation would make negative externalities appear affecting the whole neighbourhood.

The use of a huge database allows interesting findings, but one limitation can be to not have longer time observations to identify the crime evolution and its impact on housing prices.

Large implications as the relationship identified in this paper allow defining precise policies to avoid crime in different areas in LA. In addition, crime has significant but quantitative small effects on LA housing transaction prices suggesting that the effect depends on the spatial scale as well as lack on information about where the crimes are committed. Lack on information suggests low transparency in the market, affecting the transaction decision-taken process, affecting the risk perception and with relevant implications over household welfare.

This paper relates the spatial association among crimes defining the hotspots and their impacts on housing transaction prices.

There is a lack of understanding in the literature on the spatial relationships between crime and house price. This paper aims to test the impact of spatial effects in the housing market, how these are related to the incidence of crime and whether effects vary by the type of crime.

The analysis initially explores univariate and bivariate spatial patterns in crime and house price data for the Belfast Metropolitan Area using Moran’s I and Local Indicator Spatial Association (LISA) models, and secondly uses spatial autoregression models to estimate the role of crime on house prices. A spatially weighted two-stage least-squares model is specified to analyse the joint impact of crime variables. The analysis is cross sectional, based on a panel of data.

The paper illustrates that the pricing impact of crime is complex and varies by type of crime, property type and location. It is shown that burglary and theft are associated with higher-income neighbourhoods, whereas violence against persons, criminal damage and drugs offences are mainly associated with lower-priced neighbourhoods. Spatial error effects are reduced in models based on specific crime variables.

The originality of this paper is the application of spatial analysis in the study of the impact of crime upon house prices. Criticisms of hedonic price models are based on unexplained error effects; the significance of this paper is the reduction of spatial error effects achievable through the analysis of crime data.

This paper analyzes how three major industrial stock indices related to South Korean real estate industries are affected by the exogenous shock of the measures taken to control COVID-19, coupled with investor sentiment, which has global impacts.

The paper uses daily stock market indices on three major stock price indices: construction industry sector index, real estate operating company (REOC) industry index and the real estate investment trust (REIT) industry index of the Korea Stock Exchange (KRX), from January 8, 2020, when the World Health Organization (WHO) began to issue official indicators regarding COVID-19, to March 27, 2020, the last trading day of the week during which the South Korean government's stock market stabilisation fund was launched.

Results indicate the REIT sector's stock rate of return to be relatively less sensitive to impacts of COVID-19 compared to those of the two other indices. Impulse response analysis also shows similar results. Impulse response estimations indicate that earlier information of REITs has prominent significance in explaining changes in the time series process itself. Similar to findings of prior studies that have been conducted with long-term perspectives, results of our short-term study indicate that the medium-risk, medium-return characteristic of the real estate industry has significance even in short-term perspectives.

REITs can be an investment vehicle that provides strong benefits of diversified investment for mutual fund investment managers even in the case of short-term exogenous market disruptions.

The analysis run in the empirical exercise is the first to consider the sensibility between international stock exchanges to the effects of measures taken to control COVID-19 impact.

The purpose of this paper is to examine the role of monetary liquidity in house price evolution through examining the Asset (housing) Inflation channel. It identifies the main channels of transmission affecting house prices from monetary supply channels to house price change, examining how the Asset Price channel transmits changes in M1 to housing prices in Spain and the UK.

The paper uses Vector Auto Regression (VAR) and Error Correction models to test the Asset Inflation channel in the UK and Spain from 1991 to 2013 in two steps. In the first step, the supply elasticity is estimated through the long-term relationship between house prices and stock supply. The second step estimates a Vector Error Correction (VEC) to explain house price dynamics conditioned on supply reactions. The latter is defined as a long-term inverse demand model where housing prices are controlled by fundamentals in each market. Models allow forecast testing using Choleski impulse responses methodology.

Several results are found. In the supply model, both countries show rapid convergence to equilibrium with a larger elasticity of supply in Spain than in the UK but with a short run effect of new supply on prices in the UK. Regarding the Asset Inflation Channel model, the paper finds evidence of the existence of a housing accelerator effect in Spain, but not in the UK where changes in liquidity fully impact house prices in one direction.

Implications of findings are mainly to forecast the effects of Monetary Policy measures in different economies.

The model supports the evaluation of different impacts of monetary policy in territories. It shows that the same policy will have different impacts in different housing markets and therefore highlights the importance of examining each market separately to identify the appropriate policy interventions.

This is the first paper that estimates the impact of the Asset Inflation Channel on house prices that endogenises housing market conditions and compares effects and interrelationships in two different economies.

The paper develops a housing model equation for Spain and selected regions to estimate new supply elasticity. The aim of the paper is to assess the role of housing supply on price evolution and explain the fall in housing starts since the start of the credit crunch.

The paper uses a pooled EGLS specification controlling for the presence of cross-section heteroskedasticity. Fixed effect estimators are calculated to capture regional heterogeneity. The model uses secondary data (quarterly) for 17 Spanish regions over the period 1990-2012. A recursive procedure is applied to estimate model parameters starting with a baseline model (1990-1999) and successively adding one-year time information. Elasticities, as well as explanatory power from models, are reported and jointly analyzed. Elasticity is interpreted as the extent to which market mechanisms drive developer responses.

Elasticities of new supply are shown to be very stable during all periods but characterized by differences in response at a regional level. Elasticity ranges from 0.8 to 1.3 across regions. The model reports a non-market-oriented mechanism that guides building decisions. The credit crunch and debt crisis have had a double negative effect capturing the cumulative effect of exogenous shocks.

Elastic responses restrained the effects of over-pricing in the period of strong demand pressures in the early 2000s. Changes in elasticity parameters over time suggest that long-term elasticity in housing supply depends on the specific region analyzed. The results show that the credit crunch shock had varying degrees of severity in Spanish regions, dramatically reducing house-building because of the high sensitivity to changes in prices.

Estimated elasticity may be used to forecast responses to changes in housing prices. The results add to the understanding of the equilibrium mechanism in the housing market across regions.

This is the first article that analyses housing supply, calculates supply elasticities and measures the impact of the credit crunch on the housing market from the supply side in Spain. The paper adds evidence to the debate concerning the equilibrium mechanism in the housing market.