Contextualized property market models vs. Generalized mass appraisals: An innovative approach

The present research takes into account the current and widespread need for rational valuation methodologies, able to correctly interpret the available market data. An innovative automated valuation model has been simultaneously implemented to three Italian study samples, each one constituted by two-hundred residential units sold in the years 2016-2017. The ability to generate a "unique" functional form for the three different territorial contexts considered, in which the relationships between the influencing factors and the selling prices are specified by different multiplicative coefficients that appropriately represent the market phenomena of each case study analyzed, is the main contribution of the proposed methodology. The method can provide support for private operators in the assessment of the territorial investment conveniences and for the public entities in the decisional phases regarding future tax and urban planning policies

Using Genetic Algorithms for Real Estate Appraisal

The main aim of this paper is the interpretation of the existing relationship between real estate rental prices and geographical location of housing units in a central urban area of Naples (Santa Lucia and Riviera of Chiaia neighborhoods). Genetic algorithms (GA) are used for this purpose. Also, to verify the reliability of genetic algorithms for real estate appraisals and, at the same time, to show the forecasting potentialities of these techniques in the analysis of housing markets, a multiple regression analysis (MRA) was applied comparing results of GA and MRA

Multivariate Dynamic Analysis and Forecasting Models of Future Property Bubbles: Empirical Applications to the Housing Markets of Spanish Metropolitan Cities

The cogency of evaluation models able to predict future trends and to monitor the consequences of scenarios different from those initially expected has been determining a growing scientific interest for the development of financial sustainability methods. With reference to quarterly time series collected for the metropolitan area of five Spanish cities, in this research an innovative methodology has been implemented, in order to make explicit, for each case study, the main functional relationships between the housing prices and the socio-economic factors. The models obtained are characterized by both high statistical performance and compliance with the expected market phenomena, highlighting the decisive role in the housing price formation of the factors that indirectly represent the population’s income capacity (market rents, unemployment level, mortgages). Then, an empirical procedure for the construction of the future property value trends has been developed. The results point out the forecasting and monitoring potentialities of the methodology used, as a fundamental decision support tool in the urban planning policies of the local administrations, interested in anticipating and checking future housing bubbles through appropriate economic policies, and for private operators, in the phases of selection of the most attractive territorial areas for new property realizations

Who performs better? AVMs vs hedonic models

Purpose: In the literature there are numerous tests that compare the accuracy of automated valuation models (AVMs). These models first train themselves with price data and property characteristics, then they are tested by measuring their ability to predict prices. Most of them compare the effectiveness of traditional econometric models against the use of machine learning algorithms. Although the latter seem to offer better performance, there is not yet a complete survey of the literature to confirm the hypothesis. Design/methodology/approach: All tests comparing regression analysis and AVMs machine learning on the same data set have been identified. The scores obtained in terms of accuracy were then compared with each other. Findings: Machine learning models are more accurate than traditional regression analysis in their ability to predict value. Nevertheless, many authors point out as their limit their black box nature and their poor inferential abilities. Practical implications: AVMs machine learning offers a huge advantage for all real estate operators who know and can use them. Their use in public policy or litigation can be critical. Originality/value: According to the author, this is the first systematic review that collects all the articles produced on the subject done comparing the results obtained

Efficiency, Fairness and Sustainability in Social Housing Policy and Projects

The provision of affordable housing for low-income households is a very complex issue that has long been debated in many countries around the world. Social housing (SH) is one of the tools for achieving fairness, social sustainability, and economic feasibility, and it is interrelated with politics, ethics, and economics, as well as the environment, architecture, and technology. In other words, national and local policies, as well as public and private financial resources, are all needed to provide SH.SH also involves social and urban transformations and is, consequently, linked to urban planning and redevelopment projects, real estate market dynamics, and cooperation between public and private stakeholders. Furthermore, decision-making on SH policies and projects has to be supported by assessments of economic feasibility and social and environmental sustainability.This volume presents studies on various topics to recompose the multi-faceted subjects of social housing within a unified framework

Credit risk management of property investments through multi-criteria indicators

The economic crisis of 2008 has highlighted the ineffectiveness of the banks in their disbursement of mortgages which caused the spread of Non-Performing Loans (NPLs) with underlying real estate. With the methods stated by the Basel III agreements, aimed at improving the capital requirements of banks and determining an adequate regulatory capital, the banks without the skills required have difficulties in applying the rigid weighting coefficients structures. The aim of the work is to identify a synthetic risk index through the participatory process, in order to support the restructuring debt operations to benefit smaller banks and small and medium-sized enterprises (SME), by analyzing the real estate credit risk. The proposed synthetic risk index aims at overcoming the complexity of Basel III methodologies through the implementation of three different multi-criteria techniques. In particular, the integration of objective financial variables with subjective expert judgments into a participatory process is not that common in the reference literature and brings its benefits for reaching more approved and shared results in the debt restructuring operations procedure. Moreover, the main findings derived by the application to a real case study have demonstrated how important it is for the credit manager to have an adequate synthetic index that could lead to the avoidance of risky scenarios where several modalities to repair the credit debt occur

Modelling of commercial property market segmentation to improve price prediction accuracy in Malaysia

The commercial property market is strategic to the global economy. Significant attention is therefore given to its pricing by various stakeholders. The most common price modelling technique is the traditional hedonic price model. The commercial property market is too complex to be modelled by the traditional single equilibrium model. Property market segmentation models are used to improve the accuracy of price modelling, mostly reported in the housing market. This research, therefore, aims to propose a commercial property market segmentation model to improve price prediction accuracy in Malaysia. 14,043 commercial property transaction records obtained from Malaysia’s National Property Information Centre (NAPIC) was used. The submarkets were delineated using conventional hedonic, data-driven and spatial econometrics approaches. The evidence of submarket existence was determined using the Chow test and weighted RMSE, MAE and MAPE. The research found a significantly high level of spatial dependence in Malaysia’s commercial property market. Submarkets were efficiently delineated using all the methods except using submarket dummies. The research proposed the spatial error model using adaptive kernel maximum KNN spatial weight matrix as the optimal model for commercial property market segmentation in Malaysia. The proposed model improved the model fit by 19.76 per cent, reduced the RMSE, MAE and MAPE by 20.82 per cent, 24.63 per cent, and 25.92 per cent, respectively. The research shows that accounting for spatial dependence in the commercial property market reduces error, improves model fit and increases the accuracy of price modelling. The research has contributed to the existing body of knowledge by extending the commercial property market segmentation from a priori methods to the empirical data-driven and spatial econometrics approach in Malaysia. The implication to policymakers, financial institutions, the economy, property valuers, and property investors is that the findings will guide them in making informed decisions regarding the differentiated commercial property market

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

Valuation of ecological retrofitting technology in existing buildings: A real-world case study

The world’s existing buildings are aged, in a state of deterioration and in need of inter-ventions. When selecting the type of possible intervention to be applied, the choice falls between two alternatives: simple unsustainable ordinary maintenance versus ecological retrofitting i.e., an increase in the quality of the indoor environment and building energy saving using local bio-natural materials and products. The present research seeks to respond to the requests of recent comprehen-sive reviews which ask for the retrofitting of the world’s huge existing building stocks and portfolios by proposing an approach and testing it in a specific case study (at the unit, building and urban block level) which can then be carried out and repeated in the future on a larger urban scale. The real-world experimentation in the provided case study achieved the important outcome and goal of a Green Building strategy and post-carbon city framework i.e. the significant enhancement of the thermal performance of the buildings as a result of a few targeted key external works and the con-sequent saving of energy in those already existing (but not preserved and not included in the state national register or record of monuments) Liberty-style constructions. All the above show that these important existing buildings can be ecologically retrofitted at an affordable cost, although initially slightly more expensive than the cost of ordinary unsustainable maintenance. However, this difference is offset by the favorable pay-back period, which is fast, acceptable and of short duration. The tried and tested approach, the positive proposed case study and the experimental database-GIS joint platform (the details of which can be found in an additional supplementary research which is currently being carried out) are the bases on which a future decision support system will be proposed. This support system can be carried out as a tailor-made solution for the ecological retrofitting of the enormous existing building stocks and portfolios which must be considered on a larger scale i.e., at ward, quartier, city, regional and country level