An investigation into the factors that determine the price adjustment mechanism of the residential real estate market in Hong Kong

We have developed a statistical method for the valuation of residential properties using a hierarchical Bayesian approach, which takes into consideration the unique structure of the Hong Kong property market. Our model is calibrated on a dataset that covers all residential real estate transactions in ten major Hong Kong residential complexes from February 2008 to February 2009. Although parsimonious, our model outperforms other valuation methods that are based on average price-per-square- feet or expert assessments. By providing our model-based valuations online without charge, we hope to improve transparency in the Hong Kong housing market, thus enabling consumers to make better investment decisions.

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International Real Estate Review

International Real Estate Review ◽

10.53383/100319 ◽

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.

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

The world of new economy ◽

10.26794/2220-6469-2018-12-3-140-152 ◽

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.

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