Measuring the Housing Price Dispersion in Italy

Land USe Policy

The aim of this paper is to analyze the relationship between housing values and a set of determinants, related both to the urban environment and to the structural characteristics of the housing market, in the metropolitan area of Cagliari. In order to achieve this aim, a sample of residential properties spread across the urban context was taken into account. For every single residential unit we study the value of houses, identified as their estimated value, cadastral value, rent value, value supplied by the National Observatory on Real Estate Market, and finally sale value as related to factors which are identified as relevant variables in several studies concerning the real estate market. The adopted approach implies data collection concerning value and characteristics of houses. The resulting dataset is geocoded and spatially analyzed, in order to identify spatial autocorrelation of the value of houses and its correlations with respect to the characteristics of houses through the hedonic approach. The methodological approach can be easily replicated and exported with reference to other Italian and European urban contexts and results could be straightforwardly comparable. Policy implications of the findings could be a point of reference for future Italian and European planning policies concerning housing markets and the improvement of the quality of urban life.

Aestimum, 2012

2010

Journal of Housing and the Built Environment, 2011

This is an empirical study on the pricing of two vertical property attributes: floor level and building height. Floor level is the vertical location of a unit in a multi-storey building; the extra price paid for a higher floor level is labelled a floor-level premium. Previous hedonic price studies unequivocally showed that the floor-level premium is positive, but they were silent on whether its magnitude varies with floor levels and with buildings of different heights. Indeed, building height is a feature of a building, not its constituent units, so it is not clear whether building height alone should affect the units' prices. Based on a sample of highly homogeneous housing units in buildings of varying heights, we found that (1) the floor-level premium was not constant, but diminished as floor level increases; (2) there was no significant difference in the pattern of the floor-level premium between high-rise and low-rise buildings; and (3) there was a positive and significant premium for units in low-rise buildings over those in high-rise ones. These findings can help developers determine the optimal height and shape of their development.

2007

This study presents the methodology used by the Agenzia del territorio to produce real-estate indices for the analysis of the housing market in the Turinese area districts. The importance of an index is highlighted by the fact that a significant percentage of national wealth is the property sector. To this end, we use a rich and detailed database on transaction prices which allows us to study the dynamics of the residential housing market through the estimation of hedonic price indexes for Turin. This study carried out an hedonic methodology, not yet applied in Italy, on data collected and aggregates in homogeneous areas for the city of Turin. The results obtained appear to be of valid significance in the ratios, also in terms of values. Home price increased 40 percent from first semester of 2003 through the second semester of 2007. [JEL Classification: D49, D69, C4, R2]

2003

THE VALUE OF HOUSING CHARACTERISTICS A house is made up of many different components, each of which may add value to (or subtract value from) the asset. The physical attributes that determine the productive appeal of the property include the physical improvements to the site, such as the size, quality, and style of the structure and other factors such as location and age. Per standard appraisal practice, property value is usually affected by (1) physical characteristics and location, (2) conditions of sale, (3) market conditions, and (4) financing. Hedonic regression analysis is commonly used to estimate the effect of these factors on value. Regression analysis has two strengths: first, it can be used to value a large number of properties and/or factors, and, second, it can be used to explain value as well as estimate it. The ability of regression analysis to explain price means that it can be used to estimate the value of individual characteristics and their marginal contribution to the value of the property. This information can be useful to a number of market participants including homeowners seeking to sell or renovate their properties, real estate agents who may be called upon to value individual components, real estate appraisers when estimating property values, and real estate developers in understanding trends in homebuyers' taste and preferences. This study applies hedonic analysis to TReND MLS (Philadelphia area) data of detailed property characteristics, and selling prices to estimate the marginal value of individual characteristics of housing. These values can vary with consumers' tastes and preferences, the price of the property, location, etc. Knowing the marginal contribution to value of individual characteristics can allow better comparisons between similar homes and increase efficiency H. ESTIMATING THE MODEL BY AREA I. ESTIMATING THE MODEL OVER TIME

Journal of Urban Economics, 1980

Aestimum, 2012

In the study of variables affecting the determination of property prices, the spatial component is playing an increasingly significant role. In order to quantify the property value variability due to its location, it is necessary to resort to spatial statistics. The aim of this paper is twofold. On the one hand, we propose a geostatistical model aimed at identifying the incidence of position on housing asking prices. Starting from a geostatistical model we propose a methodology to empirically measure the incidence of a geographical segmentation on asking prices. The purpose of this paper is to test whether appraisers take account of the location in defining the asking prices, that represent the first signal of houses values. The proposed model is tested on a sample of residential properties, listed on the Turin real estate market. On the other hand, staring from the results of the model, the purpose of the present work is to formulate economic-estimative interpretations of the Turin real estate market dynamics.

Conventional wisdom tells us that the price level of properties should be supported by the rent they receive. This paper examines the pricing factors of properties by analyzing how individuals allocate their income to housing consumption and other goods, which in turn become the rent (or implicit rent) to support property values. Our model’s results can explain several puzzling observations in property markets, including why the variance of property appreciation rates is much higher than that of income growth rates in the same area.

Hedonic regressions are used for property price index measurement to control for changes in the quality-mix of properties transacted. The paper consolidates the hedonic time dummy approach, characteristics approach, and imputation approaches. A practical hedonic methodology is proposed that (i) is weighted at a basic level; (ii) has a new (quasi-) superlative form and thus mitigates substitution bias; (iii) is suitable for sparse data in thin markets; and (iv) only requires the periodic estimation of hedonic regressions for reference periods and is not subject to the vagrancies of misspecification and estimation issues. JEL Classification Numbers: C43, E30, E31, R31.

M. Schrenk, V. V. Popovich (eds.), P. Zeile, P. Elisei “Plan it smart. Clever solutions for smart cities - Proceedings of the 19th international conference on urban planning, regional development and information society” CORP – Competence Center of Urban and Regional Planning ISBN: 978-3-9503110-6-8, 2014

The aim of this paper is to analyze the relationship between housing values and a set of determinants, related both to the urban environment and to the structural characteristics of the housing market, in the metropolitan area of Cagliari. In order to achieve this aim, a sample of residential properties spread across the urban context was taken into account. For every single residential unit we study the value of houses, identified as their estimated value, cadastral value, rent value, value supplied by the National Observatory on Real Estate Market, and finally sale value as related to factors which are identified as relevant variables in several studies concerning the real estate market. The adopted approach implies data collection concerning value and characteristics of houses. The resulting dataset is geocoded and spatially analyzed, in order to identify spatial autocorrelation of the value of houses and its correlations with respect to the characteristics of houses through the hedonic approach. The methodological approach relates to the first four of the six conceptual features of smartness, that is economy, environment, governance, living standard, mobility and people, that characterize the theoretical framework which defines smart cities (Vanolo, 2014). Moreover, it can be easily replicated and exported with reference to other Italian and European urban contexts and results could be straightforwardly comparable. Policy implications of the findings could be a point of reference for future Italian and European planning policies concerning housing markets and the improvement of the quality of urban life

Journal of Risk and Financial Management, 2022

Housing research is one of the hot topics in many countries. This paper provides a quick review of the housing economics research in the US, Sweden, Latvia, China, Corsica, and Italy published in this special issue. Bao and Shah studied the effects of home-sharing platforms in general and the effects of the US’ Airbnb on neighbourhood rent. Wilhelmsson’s results showed that interest rates directly affected house prices and indirectly affected bank loans in Sweden. Caudill and Mixon threw light on the relative negotiating power of the buyer and seller as a key element of real estate price models. Čirjevskis presented a real application of “step-by-step” valuation options for real estate development projects as a managerial risk management tool for similar real estate development projects in the EU to make investment decisions during COVID-19 and in the post-COVID-19 era. Pelizza and Schenk-Hoppé used an exponential Ornstein–Uhlenbeck process to model price dynamics provincially and r...

The Journal of Real Estate …, 2004

In this article different spatial statistics techniques to analyze the behavior of used dwelling market prices are compared. We ®t two lattice models: simultaneous and conditional autoregressive, a geostatistical model, the socalled universal kriging and ®nally, a linear mixed-effect model. Different spatial neighborhood structures are considered, as well as different spatial weight matrices and covariance models. The results are illustrated through a real data set of 293 properties from Pamplona, Spain.

2010

The paper uses hedonic regression techniques in order to decompose the price of a house into land and structure components using readily available real estate sales data for a Dutch city. In order to get sensible results, it proved necessary to use a nonlinear regression model using data that covered multiple time periods. It also proved to be necessary to impose some monotonicity restrictions on the price of land and structures.

Working Papers, 2004

The immobility of houses means that their location affects their values. This explains the common belief that three things determine the price of a house: location, location, and location. We use this notion to develop the 3L Approach to house price determination. That is, prices are determined by the Metropolitan Statistical Area (MSA), town, and street where the house is located. This study creates a unique data set based on data from the American Housing Survey (AHS) consisting of small 'clusters' of housing units with information on structure and resident characteristics that is merged with census tract-level attributes. We use these data to test the 3L Approach: we find that all three levels of location are significant when estimating the house price hedonic equation. This indicates that the concept of ''neighborhood" is multifaceted; individuals care about their very local surroundings such as the general upkeep of their street and possibly their neighbors' characteristics (cluster variables), and a broader area such as the school district and/or the town that accounts for school quality and crime rates (tract variables). We show that price indices and evidence of discrimination and prejudice in the housing market are affected if all three levels of location are not included in the house price hedonic model.