scholarly journals HEDONIC PRICE INDICES AS A WAY OF DETERMINING PRICE CHANGES IN THE RESIDENTIAL REAL ESTATE MARKET

2017 ◽  
Vol 86 ◽  
pp. 423-434
Author(s):  
Urszula Gierałtowska ◽  
Putek-Szeląg Ewa
Keyword(s):  
Real Estate ◽  
Real Estate Market ◽  
Hedonic Price ◽  
Residential Real Estate ◽  
Price Indices ◽  
Price Changes ◽  
Hedonic Price Indices

Analysis of the residential real estate market on the example of households and apartments of the secondary market of the city of Krasnodar

2020 ◽  
pp. 118-122
Author(s):  
Б.А. Хахук ◽  
Е.Ч. Куадже
Keyword(s):  
Real Estate ◽  
Real Estate Market ◽  
Secondary Market ◽  
Residential Real Estate ◽  
Price Changes ◽  
Location Factor ◽  
Factors Affecting ◽  
Main Factors ◽  
The City

Статья посвящена исследованию рынка жилой недвижимости в МО г. Краснодар, представлена динамика изменения цен за период 2009-2019 гг. Выявлены основные факторы, влияющие на формирование стоимости объектов жилой недвижимости в городе Краснодаре, при этом основное внимание уделено фактору местоположение. The article is devoted to the study of the residential real estate market in the city of Krasnodar, the dynamics of price changes for the period 2009-2019 is presented. The main factors affecting the formation of the value of residential real estate in the city of Krasnodar are identified, with the focus on the location factor.

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

2014 ◽  
Vol 587-589 ◽  
pp. 2176-2182 ◽  
Author(s):  
Vincenzo del Giudice ◽  
Pierfrancesco de Paola
Keyword(s):  
Real Estate ◽  
Road Traffic ◽  
Additive Model ◽  
Noise Pollution ◽  
Real Estate Market ◽  
Sound Level ◽  
Economic Effects ◽  
Hedonic Price ◽  
Residential Real Estate ◽  
Hedonic Price Function

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).

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.

scholarly journals Did the COVID-19 Pandemic Crisis Affect Housing Prices Evenly in the U.S.?

2021 ◽  
Vol 13 (21) ◽  
pp. 12277
Author(s):  
Xinba Li ◽  
Chuanrong Zhang
Keyword(s):  
Real Estate ◽  
Spatial Patterns ◽  
Price Change ◽  
Housing Prices ◽  
Housing Price ◽  
Real Estate Market ◽  
Moran’S I ◽  
Moran's I ◽  
Price Changes ◽  
The U.S

While it is well-known that housing prices generally increased in the United States (U.S.) during the COVID-19 pandemic crisis, to the best of our knowledge, there has been no research conducted to understand the spatial patterns and heterogeneity of housing price changes in the U.S. real estate market during the crisis. There has been less attention on the consequences of this pandemic, in terms of the spatial distribution of housing price changes in the U.S. The objective of this study was to explore the spatial patterns and heterogeneous distribution of housing price change rates across different areas of the U.S. real estate market during the COVID-19 pandemic. We calculated the global Moran’s I, Anselin’s local Moran’s I, and Getis-Ord’s statistics of the housing price change rates in 2856 U.S. counties. The following two major findings were obtained: (1) The influence of the COVID-19 pandemic crisis on housing price change varied across space in the U.S. The patterns not only differed from metropolitan areas to rural areas, but also varied from one metropolitan area to another. (2) It seems that COVID-19 made Americans more cautious about buying property in densely populated urban downtowns that had higher levels of virus infection; therefore, it was found that during the COVID-19 pandemic year of 2020–2021, the housing price hot spots were typically located in more affordable suburbs, smaller cities, and areas away from high-cost, high-density urban downtowns. This study may be helpful for understanding the relationship between the COVID-19 pandemic and the real estate market, as well as human behaviors in response to the pandemic.

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