Pierfrancesco De Paola

Geoadditive Models for Property Market

Geoadditive models represent efficient and flexible tools, useful in modeling realistically complex situations. Mainly they are based on semi-parametric regressions often integrated by Kriging techniques for the spatial interpolation of surfaces. One of the choices to be made for determination of interpolated surfaces regards the specific function to be used to estimate the unknown values. The choice may currently occur between exponential, gaussian, linear, rational or spherical functions. In this working paper a geoadditive model based on penalized spline functions has been proposed, in order to obtain improvements in forecasting of interpolated surfaces respect to usual Kriging techniques. The main aim of this study is the identification of methodology in able to define and delineate the real estate market scenarios for urban areas through analysis of property values and their spatial distribution.

Linear Programming in a Multi-Criteria Model for Real Estate Appraisal

In real estate appraisal, research has long been addressed to the experimentation of multi-parametric models able to reduce the margin of error of the estimate and to overcome or to limit, as far as possible, the problems and difficulties that the use of these models often involves. On the one hand, researchers are trying to overcome the essentially deductive approach that has characterized the traditional discipline, and on the other, to minimize the problems arising from a merely inductive approach. The real estate market is characterized by an inelastic supply and by properties whose complexity and differentiation often involve, also and especially on the demand side, subjective and psychological elements that could distort the results of an inductive investigation. This problem can be overcome by increasing the size of the survey sample, and by using statistical analysis. Statistical analyses, however, are often based on very strong assumptions. A multi-criteria valuation model that uses linear programming is applied to the real estate market. The model, integrated with the inductive and deductive approach, exceeds many of the assumptions of the best known statistical approaches.

Hedonic Analysis of Housing Sales Prices with Semiparametric Methods

This study estimates a hedonic price function using a semiparametric regression based on Penalized Spline Smoothing, and compares the price prediction performance with conventional parametric models. The excellent results obtained show that the semiparametric models allow to obtain a significant improvement in the prediction of housing sales prices.

Spline Smoothing for Estimating Hedonic Housing Price Models

The exact prediction of housing selling prices is a relevant issue for real estate market, also to evaluate alternative forms of financial investment. In this paper a hedonic price function built through a semiparametric additive model is implemented. This model use penalized spline functions and aims to achieve a significant improvement in the prediction of the market price of the properties.

The Effects of Noise Pollution Produced by Road Traffic of Naples Beltway on Residential Real Estate Values

Noise pollution generated by road traffic represents a damage factor for property values when sound pressure levels exceeds normal tolerability limit. In fact, noise emissions over the normal tolerability limit cause a real estate values reduction and lower marketability in terms of willingness to pay by traders. In this study the effects of noise pollution produced by road traffic of Naples Beltway on residential real estate values for a central urban area have been evaluated. These economic effects were evaluated using an econometric analysis of property prices (Land Price Analysis) based on a hedonic price function built through a semiparametric additive model (Penalized SplineSemiparametric Method) and applied to a sample of defined residential real estate market of Naples. In line with indications provided by wide literature examined, for increase of an sound level unit (expressed in dB) it was verified that average depreciation percentage for real estate values ranges from 0,30% (diurnal emissions) to 0,33% (nocturnal emissions).

Depreciation Methods for Firm’s Assets

This study focuses on the analytical description of depreciation methods applied to firm’s equipments for corporate accounting and balance sheet. Depreciation is a systematic allocation of fixed asset cost over its useful life. Several methods are applied to estimate depreciation cost: Straight-Line Method, Sum of Years Digits Method, Declining Balance Method, Declining Balance Method switched to Straight-Line Method, Interest Methods (Sinking Fund Method and Annuity Method), Usage Methods (Machine Hours Method and Production Units Method), Depletion Method. Each of these depreciation methods have been examined in detail, concluding this work with an analytical and critical comparison between them.

Undivided Real Estate Shares: Appraisal and Interactions with Capital Markets

The appraisal of undivided and indivisible real estate shares represents a recurring and underestimated issue by professional appraisers. This problem requires a logical solution considering that the undivided real estate shares are more difficult to sell and, consequently, there is a decrease of their market value. It follows that issue can be referred, in theoretical and practical terms, to the real estate investment risk valuation and on how to convert this risk into an expected rate that can compensate it. In this paper the Capital Asset Pricing Model has been integrated with Penalized Spline Semiparametric Method in order to obtain an algorithm that allows to rationalize the appraisal of undivided real estate shares using easily accessible data.

Spatial Analysis of Residential Real Estate Rental Market with Geoadditive Models

A study of geographical variability of real estate rents in the central urban area of Naples (Italy) benefits from geostatistical mapping or kriging. Often, some of the observed variables can have non-linear relationships with the response variable. To account for such effects properly we combine kriging techniques with additive models to obtain the geoadditive models, expressing both as linear mixed models. The resulting mixed model representation for the geoadditive model allows for fitting and analysis using standard methodology and software. In effect, the geoadditive models represent efficient and flexible tools, useful in modeling realistically complex situations, often based on semi-parametric regressions integrated by Kriging techniques for the spatial interpolation. In this paper a geoadditive model based on penalized spline functions has been applied, in order to obtain improvements respect to usual Kriging techniques, an analysis of rents values and their spatial distribution for the neighborhoods of Chiaia and Santa Lucia in Naples.

Post Carbon City: Building Valuation and Energy Performance Simulation Programs

Today, world carbon energy consumption has increased dramatically. The survival of the Earth is endangered by pollution, produced by excessive oil and carbon over use. The sector that consumes this 40% of total energy is construction which needs innovative models for integrated ecological- energy - economic forecast. The research set up an integrated model for the overall assessment of a building having alternative characteristics: sustainable, vs. unsustainable or Common or Business As Usual BAS. The research takes into consideration the energy consumption for the thermal management and climate metabolism of the buildings and the consequent impacts in terms of CO2 emissions. The results obtained validate the adoption of ecological cork panels for passivation and insulation in sustainable building vs. common. The research scientifically and accurately quantifying two alternative (sustainable vs. BAS) prototype buildings, comparatively testing three Energy Performance Simulation Programs (Energy Plus; Termus; Blumatica Energy) ascertaining the coherence and convergence of all their output and results.

Geographically Weighted Regression for the Post Carbon City and Real Estate Market Analysis: A Case Study

Geographically Weighted Regression is a statistical technique for real estate market analysis, particularly adequate in order to identify homogeneous areas and to define the marginal contribution that the geographical location gives to the market value of the properties. In this paper a GWR has been applied, in order to verify the robustness of the real estate sample, this for the subsequent individuation of progressive real estate sub-samples in able to detect and to identify possible potential market premium in real estate exchange and rent markets for green buildings [21, 22, 23, 24, 25, 26, 27, 28]. The model has been built on a large real estate dataset, related to the trades of residential real estate units in the city of Reggio Calabria (Calabria region, Southern Italy).