scholarly journals What Do Real Estate Brokers Do: An Examination of Excess Returns in the Housing Market

1994 ◽  
Vol 3 (4) ◽  
pp. 283-295 ◽  
Author(s):  
G.Donald Jud ◽  
Daniel T. Winkler
Keyword(s):  
Real Estate ◽  
Housing Market ◽  
Excess Returns ◽  
Real Estate Brokers

The Housing Market and Real Estate Brokers

1995 ◽  
Vol 23 (2) ◽  
pp. 161-185 ◽  
Author(s):  
Suryamani Mantrala ◽  
Edward Zabel
Keyword(s):  
Real Estate ◽  
Housing Market ◽  
Real Estate Brokers

Perceptions of real-estate brokers and buyers: A sales-orientation, customer-orientation approach

1988 ◽  
Vol 17 (2) ◽  
pp. 175-187 ◽  
Author(s):  
B.J. Dunlap ◽  
Michael J. Dotson ◽  
Terry M. Chambers
Keyword(s):  
Real Estate ◽  
Customer Orientation ◽  
Real Estate Brokers

scholarly journals Multi Layer Perceptron Modelling in the Housing Market

2020 ◽  
Author(s):  
Ku Ruhana Ku Mahamud ◽  
Azuraliza Abu Bakar ◽  
Norita Md. Norwawi
Keyword(s):  
Regression Analysis ◽  
Real Estate ◽  
Housing Market ◽  
Predictive Ability ◽  
Multi Layer Perceptron ◽  
Kuala Lumpur ◽  
Mlp Model

The study examines the use of multi layer perception network (MLP) in predicting the price of terrace houses in Kuala Lumpur (KL). Nine factors that significantly influence the price were used in this attempt. Housing data from 1994 to 1996 were presented to the network for training. Tested results from the model obtained for various years were compared using regression analysis. The study provides the predictive ability of the trained MLP model that can be used as an alternative predictor in real estate analysis.  

scholarly journals Who Bears the Burden of Real Estate Transfer Taxes? Evidence from the German Housing Market

2021 ◽  
Author(s):  
Mathias Dolls ◽  
Clemens Fuest ◽  
Carla Krolage ◽  
Florian Neumeier
Keyword(s):  
Real Estate ◽  
Housing Market ◽  
German Housing Market

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.

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