The residential real estate market in Italy: a spatio-temporal analysis

2018 ◽  
Vol 53 (5) ◽  
pp. 2451-2472 ◽  
Author(s):  
Monica Palma ◽  
Claudia Cappello ◽  
Sandra De Iaco ◽  
Daniela Pellegrino
Keyword(s):  
Real Estate ◽  
Temporal Analysis ◽  
Real Estate Market ◽  
Residential Real Estate ◽  
Spatio Temporal

scholarly journals International Real Estate Review

2021 ◽  
Vol 24 (2) ◽  
pp. 139-183
Author(s):  
Kristoffer B. Birkeland ◽  
◽  
Allan D. D’Silva ◽  
Roland Füss ◽  
Are Oust ◽  
...  
Keyword(s):  
Real Estate ◽  
Real Estate Market ◽  
Percentage Error ◽  
Residential Real Estate ◽  
Valuation Model ◽  
Stacked Generalization ◽  
Novel Approach ◽  
Out Of Sample ◽  
Selection For ◽  
Automated Valuation

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.

scholarly journals Assessment of Average Market Investment Returns in Development of the Residential Real Estate in the Moscow Region

2019 ◽  
Vol 12 (3) ◽  
pp. 140-152
Author(s):  
S. G. Sternik ◽  
Ya. S. Mironchuk ◽  
E. M. Filatova
Keyword(s):  
Real Estate ◽  
Initial Data ◽  
Housing Market ◽  
Return On Investment ◽  
High Reliability ◽  
Real Estate Market ◽  
Moscow Region ◽  
Residential Real Estate ◽  
Market Return ◽  
Total Investment

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.

The Residential Real Estate Market Influence on the Reconstruction and Housing Restoration Potential in the Region

2018 ◽  
Vol 931 ◽  
pp. 1204-1209
Author(s):  
Victoria G. Sevka ◽  
Viktoriya V. Panchenko ◽  
Elena A. Kilimnik
Keyword(s):  
Real Estate ◽  
Life Quality ◽  
Real Estate Market ◽  
Residential Real Estate ◽  
Market Situation ◽  
Restoration Potential

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.

Estimation of Risk-Return for Real Estate Investments by Applying Ellwood’s Model and Real Options Analysis: An Application to the Residential Real Estate Market of Naples

2014 ◽  
Vol 651-653 ◽  
pp. 1570-1575 ◽  
Author(s):  
Vincenzo del Giudice ◽  
Alfredo Passeri ◽  
Pierfrancesco de Paola ◽  
Francesca Torrieri
Keyword(s):  
Real Estate ◽  
Real Options ◽  
Financial Analysis ◽  
Real Estate Market ◽  
Residential Real Estate ◽  
Real Options Analysis ◽  
Risk Return ◽  
Real Estate Investments ◽  
Capitalization Rate ◽  
Financial Income

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.

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