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This paper aims to present the dynamics of housing prices in Italian cities based on unpublished data with regional details from the late 1960s, half-yearly base, for all main Italian cities measuring the average prices for three city dimensions: city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.

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.

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.

Theory and empirical research show how building cycles’ behaviour has substantial differences within countries among its different geographical areas. There is evidence of the existence of specific area leadership regarding development activity and how this influence is transmitted to the rest of the country as a locomotive effect in residential construction. This means that the aggregate building cycle could strongly depend on cycles in specific areas. This paper follows this approach and investigates the relationship between geographic areas’ intensity in housing construction, showing how activity development in some of these areas is influencing the rest of the country. This process is analysed in Spain during the 1990s, using information on house licenses of construction given by regional areas and applying cointegration methodology to identify leading areas in building activity. Some leadership effect is found in the Levante area attracting building activity from the rest of the country during this period.

Residential price levels in Spain vary broadly among markets. Real estate theory explains that prices depend on market characteristics such as vacancy level, land availability, construction supply elasticity to respond to high or low speed to changes on the demand, as well as potential for economic growth, industrial and services activities located inside urban areas, etc. An analysis of prices in Spanish main cities shows that tensions appear to exist in some of them where economic activity shows different dynamism and price level appears to be independent of it. This paper tries to find evidence of the existing relationship between residential prices and economic and demographic factors that are demand determinants such as wages, migrations and productive structure, among others, to explain price formation in Spanish cities. It uses panel data and GLS methodology applied to 71 main Spanish province capitals and cities with more than 100,000 inhabitants. The results show evidence of determinants of housing prices and how some relationships appear to exist between price levels and families’ waged income as well as with population and productive structure in Spanish cities.