LO SPIN-OFF UNIVERSITARIO “URBAN LAB S.R.L.” STRUMENTI E METODI PER LE TRASFORMAZIONI URBANE IN PPP

The LaborEst has made available in to the territory, the enormous expertise and the scientific production with the intention of encourage and achieve virtuous processes for the territorial development, since its establishment. This action was carried out, always, by the involvement of local authorities, by trade associations and other stakeholders, working in the Calabria area, and more specifically, in the Metropolitan Area of the Strait. In this scenario, among other initiatives, starts the spin-off UrbanLab s.r.l, an initiative in the area of construction services companies to develop methods and models to support the sustainability research and the feasibility of urban transformation projects. These methods and models can be implemented in public-private partnership, offering an innovative service characterized by an economic-estimate service, computerised systems and related to feasibility and pre-feasibility studies.  DOI: http://dx.medra.org/10.19254/LaborEst.09.1

Real estate appraisals with Bayesian approach and Markov Chain Hybrid Monte Carlo Method: An application to a central urban area of Naples

This paper experiments an artificial neural networks model with Bayesian approach on a small real estate sample. The output distribution has been calculated operating a numerical integration on the weights space with the Markov Chain Hybrid Monte Carlo Method (MCHMCM). On the same real estate sample, MCHMCM has been compared with a neural networks model (NNs), traditional multiple regression analysis (MRA) and the Penalized Spline Semiparametric Method (PSSM). All four methods have been developed for testing the forecasting capacity and reliability of MCHMCM in the real estate field. The Markov Chain Hybrid Monte Carlo Method has proved to be the best model with an absolute average percentage error of 6.61%

Bare ownership evaluation. Hedonic price model vs. Artificial neural network

In the current downturn of European real estate market, the sale of the bare ownership is creating a growing interest of market operators. The attention to this formula is not only local, but also comes from foreign investors, attracted by the substantial saving on the purchase price, the revaluation of the property over time, as well as the breakdown of the tax burden and the costs of management and maintenance. The aim of this research is, first of all, to deepen the knowledge of the bare ownership market of residential properties. Secondly, it is to develop an effective tool for estimating the bare ownership. Therefore, on the same database and with reference to the same explanatory variables of the price of bare ownership, two estimation models are implemented, one based on hedonic prices theory and another using artificial neural networks (ANN), in order to compare the respective performance.In the current downturn of European real estate market, the sale of the bare ownership is creating a growing interest of market operators. The attention to this formula is not only local, but also comes from foreign investors, attracted by the substantial saving on the purchase price, the revaluation of the property over time, as well as the breakdown of the tax burden and the costs of management and maintenance. The aim of this research is, first of all, to deepen the knowledge of the bare ownership market of residential properties. Secondly, it is to develop an effective tool for estimating the bare ownership. Therefore, on the same database and with reference to the same explanatory variables of the price of bare ownership, two estimation models are implemented, one based on hedonic prices theory and another using artificial neural networks (ANN), in order to compare the respective performance

Benchmarking multi-criteria evaluation methodology's application for the definition of benchmarks in a negotiation-type public-private partnership. A case of study: The integrated action programmes of the Lazio Region

The growing scarcity of public financial in Italy, in opposition of the more significant problems of degradation of many urban areas, prompted the Legislature to standardise new processes of settlement transformation based on negotiation-type public-private partnerships (PPPN). However, these standards have not provided for benchmarks referring to the contents of partnerships or assessment procedures aimed at assessing the initiatives undertaken with respect to public utility objectives. This has often led to redevelopment initiatives geared more towards the satisfaction of private rather than public interests. The proposed methodology, structured on the integration of a benchmarking process with multi-criteria evaluation techniques known as benchmarking multi-criteria evaluation (BME) enables the definition of benchmarks through a participatory process of the different stakeholders involved in a PPPN to which the BME is applied. In order to verify the applicability of the proposed procedure, it has been applied to a type of PPPN: the integrated action programmes (PII) in the Lazio Region. The benchmarks can be used by Lazio’s administrators both for renewing the planning of the PII concerned and for verifying the quality of the initiatives within the same PPPN proces

Metodi fisico matematici avanzati per l’implementazione di modelli previsionali applicabili a fenomeni acustici e di interesse ingegneristico

2015 - 2016In several engineering fields, it is of great interest the development of models able to produce forecasts of univariate time series; these models are based on the statistical analysis of the sequence of observed data equidistant in time. The techniques implemented in this thesis can be classified in two distinct types, different but complementary: the first method is based on the analysis of the observed time series composed by measurements under study, the other method is based on Poisson's distributions for events of exceedance of a defined threshold. The validity of such models has been tested on a noise dataset collected in the city of Messina. The measurements are based on day and night noise levels, detected at a monitoring station set up by the local government and made public on a special web platform. From this set of data, several intervals have been extracted for the calibration of the models, in order to test the validity on real measurements (by means of comparison between the observed and predicted data) and to study the sensitivity with respect to variation of the parameters (reference threshold, frequency of events, periodicity of the series, etc.). The first adopted techniques, used to analyse the time series, are based on deterministic decomposition methods: the observed sequences are divided in trend and seasonal components. In this field, an enhancement of the preliminary forecasting model has been obtained: in particular, a set of electricity consumption data has been studied. This time series of absorbed electricity is due to the public transport system of the city of Sofia (Bulgaria): the main enhancement achieved is the improving of the extracted information from the series thanks to the introduction of an additional coefficient of seasonality. Later, seasonal stochastic models were adopted, of the auto-regressive moving average (SARIMA) type. Therefore, the research focused on the implementation of predictive models of stochastic type: the seasonal ARIMA was applied to the prediction of wind speed in a site where a wind farm for the production of electricity is installed. Subsequently, acoustical models have been applied for the prediction of noise produced by the turbines under certain wind speed conditions. A detailed investigation was performed with the aim to improve the integration of linear and non-linear forecasting techniques using artificial neural networks. In particular, one of the more advanced predictive model based on time series analysis is a hybrid model that uses in cascade deterministic methods, based on the decomposition of the series into trend and seasonal components, followed by a modelling via artificial neural networks for a better prediction of the non-linear part of the series. A predictive model, useful to study events of exceedance of noise thresholds, has also been implemented. This model is based on the assumption that the exceedance events are distributed according to a nonhomogeneous Poisson distribution. This approach can be pursued both with frequentist techniques or using Bayesian estimation of the parameters of the "Probability Density Function" (PDF). In particular, it has been studied a sound levels dataset measured near the international airport of Nice (France). The adopted model introduces the single "change-point" methodology for the estimation of the distribution parameters. These parameters have been estimated through a Markov-Chain Monte-Carlo sampling based on Bayesian statistical assumptions. [edited by author]In diversi ambiti ingegneristici risulta di grande interesse lo sviluppo di modelli atti a produrre previsioni di serie storiche univariate mediante l’analisi della successione di dati osservati equidistanti nel tempo. Le tecniche implementate nel presente lavoro di tesi possono essere classificate in due distinte tipologie, differenti ma complementari: una basata sull’analisi delle serie storiche delle misure di interesse, l’altra su distribuzioni di Poisson per gli eventi di superamento di una soglia stabilita. La validità di siffatti modelli è stata testata su un set di dati di rumore raccolti nella città di Messina. Le misurazioni si riferiscono a livelli acustici diurni e notturni, rilevati presso una stazione di monitoraggio predisposta dall’amministrazione locale e resi pubblici su apposita piattaforma web. Da questo set di dati, sono stati estratti diversi intervalli per la calibrazione dei modelli, al fine di testarne la validità su misurazioni reali (mediante confronto tra dato osservato e dato previsto) e di studiare la sensibilità rispetto alla variazione dei parametri (soglia di riferimento, frequenza degli eventi, periodicità, ecc.). Per l’analisi delle serie storiche sono state sviluppate tecniche classiche basate sulla decomposizione deterministica nelle componenti di trend e stagionali di una sequenza di dati osservata. Si è in seguito ottenuto un potenziamento del modello di previsione e analisi delle serie storiche: in particolare si è analizzato un set di dati di assorbimento di energia elettrica dovuto al sistema di trasporto pubblico della città di Sofia, migliorando l’estrazione di informazioni dalla serie e le prestazioni grazie all’introduzione di un ulteriore coefficiente di stagionalità. Successivamente sono stati adottati modelli stocastici stagionali auto-regressivi a media mobile (SARIMA); dunque ci si è concentrati sull’implementazione di modelli previsionali stocastici del tipo Seasonal ARIMA applicati alla previsione della velocità del vento in un sito dove è installato un impianto per la produzione elettrica mediante aerogeneratori. In seguito si sono applicati modelli per la previsione dell’inquinamento acustico prodotto dal parco eolico investito da vento ad una certa velocità. Si è inoltre migliorata l’integrazione di tecniche previsionali lineari e non lineari mediante reti neurali artificiali; in particolare lo stato dell’arte per i modelli previsionali basati sull’analisi di serie storiche si è raggiunto con un modello ibrido basato sull’utilizzo in cascata di metodi classici deterministici basati sulla scomposizione della serie in componenti di trend e stagionalità seguiti da modellazione tramite reti neurali artificiali per una migliore previsione della parte non lineare della serie. È stato inoltre implementato un modello di previsione per eventi di superamento di soglie di inquinamento acustico. Tale modello è basato sull’assunzione che gli eventi di superamento sono distribuiti secondo una distribuzione di Poisson non omogenea. Questo approccio può essere a sua volta perseguito con tecniche frequentiste o bayesiane per la stima dei parametri della “Probability Density Function” (PDF). In particolare è stato studiato un dataset di misurazioni fonometriche acquisite in prossimità dell’aeroporto internazionale di Nizza (Francia): il modello previsionale realizzato prevede l’introduzione della metodologia “change-point” singolo per la stima dei parametri della distribuzione. Tali parametri sono stati stimati grazie al campionamento Monte-Carlo Markov-Chain basato su assunzioni di statistica bayesiana. Infine si è studiato un potenziamento di questo modello previsionale applicandolo al set di dati di rumore acustico misurati nella città di Messina: tale serie storica è stata prima ricostruita integralmente tramite le tecniche previsionali studiate in precedenza e dopo si è applicato il modello bayesiano basato sulla distribuzione di Poisson utilizzando “change-points” multipli. [a cura dell'autore]XV n.s. (XXIX

Sustainable Real Estate: Management, Assessment and Innovations

Production and consumption activities have determined a weakness of the sustainable real estate economy. The main problems are the subordination of public decision making, which is subjected to pressure from big companies; inefficient appraisal procedures; excessive use of financial leverage in investment projects; the atypical nature of markets; income positions in urban transformations; and the financialization of real estate markets, with widespread negative effects. A delicate role in these complex problems is assigned to real estate appraisal activities, called to make value judgments on real estate goods and investment projects, the prices of which are often formed in atypical real estate markets, giving ever greater importance to sustainable development and transformation issues. This Special Issue is dedicated to developing and disseminating knowledge and innovations related to most recent real estate evaluation methodologies applied in the fields of architecture and civil, building, environmental, and territorial engineering. Suitable works include studies on econometric models, sustainable building management, building costs, risk management and real estate appraisal, mass appraisal methods applied to real estate properties, urban and land economics, transport economics, the application of economics and financial techniques to real estate markets, the economic valuation of real estate investment projects, the economic effects of building transformations or projects on the environment, and sustainable real estate