Clustering Methods for Italian Residential Real Estate Market

2005 ◽  
2021 ◽  
Vol 24 (2) ◽  
pp. 139-183
Author(s):  
Kristoffer B. Birkeland ◽  
◽  
Allan D. D’Silva ◽  
Roland Füss ◽  
Are Oust ◽  
...  

We develop an automated valuation model (AVM) for the residential real estate market by leveraging stacked generalization and a comparable market analysis. Specifically, we combine four novel ensemble learning methods with a repeat sales method and tailor the data selection for each value estimate. We calibrate and evaluate the model for the residential real estate market in Oslo by producing out-of-sample estimates for the value of 1,979 dwellings sold in the first quarter of 2018. Our novel approach of using stacked generalization achieves a median absolute percentage error of 5.4%, and more than 96% of the dwellings are estimated within 20% of their actual sales price. A comparison of the valuation accuracy of our AVM to that of the local estate agents in Oslo generally demonstrates its viability as a valuation tool. However, in stable market phases, the machine falls short of human capability.


2019 ◽  
Vol 12 (3) ◽  
pp. 140-152
Author(s):  
S. G. Sternik ◽  
Ya. S. Mironchuk ◽  
E. M. Filatova

In the previous work, G.M. Sternik and S.G. Sternik justified the options for the method of assessing the average current annual return on investment in residential real estate development, depending on the nature and content of the initial data on the costs contained in the sources of information (construction costs or total investment costs). Based on the analysis of the composition of the elements of development costs used in various data sources, we corrected the coefficients that allowed us to move from the assessment of the current annual return on investment in development in relation to the cost (full estimated cost) of construction to the assessment of the current annual return on investment in relation to the total investment costs. This calculation method was tested on the example of the housing market inMoscow. As a result, we concluded it is possible its use for investment management in the housing market. In this article, based on G.M. Sternik and S.G. Sternik’s methodology for assessing the return on investment into the development, and taking also into account the increase of information openness of the real estate market, we improved the calculation formulas, using new sources of the initial data, and recalculated the average market return on investment into the development of residential real estate in the Moscow region according to the data available for 2014–2017. We concluded that, since 2015, the average market return on investment takes negative values, i.e. the volume of investment in construction exceeds the revenue from sales in the primary market. However, in the second half of 2017, the indicator has increased to positive values, which was due to a greater extent of the decrease in the volume of residential construction in the region. The data obtained by us, together with the improved method of calculations, allow predicting with high reliability the potential of the development of the regional markets of primary housing for the purpose of investment and state planning of housing construction programs.


2018 ◽  
Vol 931 ◽  
pp. 1204-1209
Author(s):  
Victoria G. Sevka ◽  
Viktoriya V. Panchenko ◽  
Elena A. Kilimnik

The article aims to research the housing state of Donetsk. The market sales and market purchase have been analysed. The main problems of the residential real estate market has been revealed. The necessity in restoration of housing has been determined by means of primary measures. These measures are directed to stabilise the market situation and to improve the life quality.


2014 ◽  
Vol 651-653 ◽  
pp. 1570-1575 ◽  
Author(s):  
Vincenzo del Giudice ◽  
Alfredo Passeri ◽  
Pierfrancesco de Paola ◽  
Francesca Torrieri

In the present study was developed an application of a model derived from Ellwood’s financial analysis and Real Options Analysis for estimating the risk-return in the residential real estate market of Naples. With the aim of reducing the uncertainty related to the determination of the risk and return for an property investment, starting of real estate investment layout derived from the Ellwood’s model, latter defined by financial income and costs related to the period of property availability, a risk analysis with Real Options Analysis has been implemented, in order to obtain the evolution of the investment value until the year in which it is convenient to recover the initial capital. This model has allowed to determine a capitalization rate for a general area of reference, that it can adapt on further effects of specific factors and intrinsic characteristics of the property being valued. It also allows to define uniquely the investment duration, in terms of availability period of property.


2017 ◽  
Vol 17 (3) ◽  
pp. 153-165
Author(s):  
Reymard Savio Sampaio de Melo ◽  
Ariovaldo Denis Granja

Abstract This study focuses on the problems associated with the traditional practice of reducing costs in construction and the need to increase business competitiveness in the residential real estate sector. In this context, target costing is a promising approach to improve the competitiveness of companies by ensuring that the products launched on the market do not jeopardize the company's results and value delivery to customers. However, far too little attention is paid to target costing implementation by companies that develop residential real state products for sale and face strong market competition. Thus, this paper seeks to investigate whether the standard framework of target costing in the literature applies - with or without adjustments - to real estate developers. Case study was the research strategy adopted. Guidelines are proposed for the introduction of target costing in the development process of residential real estate products. The proposed guidelines are related to the three main sections of the target costing process: market-driven costing, product-level target costing and component-level target costing.


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