How does ethnic discrimination on the housing market differ across neighborhoods and real estate agencies?

2021 ◽  
pp. 101820
Author(s):  
Abel Ghekiere ◽  
Pieter-Paul Verhaeghe
Keyword(s):  
Real Estate ◽  
Housing Market ◽  
Ethnic Discrimination

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.

Neighborhood Watch: The Rise of Zillow

2017 ◽  
pp. 1-7
Author(s):  
Russell Walker
Keyword(s):  
Real Estate ◽  
Information Asymmetry ◽  
Housing Market ◽  
Real Estate Industry ◽  
Neighborhood Watch ◽  
Agent Model ◽  
The Real ◽  
The Real Estate ◽  
Real Estate Agent ◽  
Principal Agent Problem

Read any news report on the housing market, and inevitably it will include facts or figures from the real estate data giant Zillow.com. The company initially set out to solve two key economic frictions in the real estate industry information asymmetry and the principal-agent problem by empowering users to access real-time housing data and eliminating the need for realtors. The company soon realized, however, that American homeowners and buyers were not willing to give up the traditional real estate agent model and changed course. In the end, Zillow decided to join rather than replace the middlemen in the real estate industry.

Property investor decisions using income and rental ratio signals

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Billie Ann Brotman
Keyword(s):  
Real Estate ◽  
Housing Market ◽  
Discount Rate ◽  
Price Index ◽  
Housing Prices ◽  
Housing Price ◽  
Consumer Sentiment ◽  
Content Type ◽  
Income Ratio ◽  
Real Estate Prices

PurposeThis paper, a case study, aims to consider whether the income ratio and rental ratio tracks the formation of residential housing price spikes and their collapse. The ratios are measuring the risk associated with house price stability. They may signal whether a real estate investor should consider purchasing real property, continue holding it or consider selling it. The Federal Reserve Bank of Dallas (Dallas Fed) calculates and publishes income ratios for Organization for Economic Cooperation and Development countries to measure “irrational exuberance,” which is a measure of housing price risk for a given country's housing market. The USA is a member of the organization. The income ratio idea is being repurposed to act as a buy/sell signal for real estate investors.Design/methodology/approachThe income ratio calculated by the Dallas Fed and this case study's ratio were date-stamped and graphed to determine whether the 2006–2008 housing “bubble and burst” could be visually detected. An ordinary least squares regression with the data transformed into logs and a regression with structural data breaks for the years 1990 through 2019 were modeled using the independent variables income ratio, rent ratio and the University of Michigan Consumer Sentiment Index. The descriptive statistics show a gradual increase in the ratios prior to exposure to an unexpected, exogenous financial shock, which took several months to grow and collapse. The regression analysis with breaks indicates that the income ratio can predict changes in housing prices using a lead of 2 months.FindingsThe gradual increases in the ratios with predetermine limits set by the real estate investor may trigger a sell decision when a specified rate is reached for the ratios even when housing prices are still rising. The independent variables were significant, but the rent ratio had the correct sign only with the regression with time breaks model was used. The housing spike using the Dallas Fed's income ratio and this study's income ratio indicated that the housing boom and collapse occurred rapidly. The boom does not appear to be a continuous housing price increase followed by a sudden price drop when ratio analysis is used. The income ratio is significant through time, but the rental ratio and Consumer Sentiment Index are insignificant for multiple-time breaks.Research limitations/implicationsInvestors should consider the relative prices of residential housing in a neighborhood when purchasing a property coupled with income and rental ratio trends that are taking place in the local market. High relative income ratios may signal that when an unexpected adverse event occurs the housing market may enter a state of crisis. The relative housing prices to income ratio indicates there is rising housing price stability risk. Aggregate data for the country are used, whereas real estate prices are also significantly impacted by local conditions.Practical implicationsRatio trends might enable real estate investors and homeowners to determine when to sell real estate investments prior to a price collapse and preserve wealth, which would otherwise result in the loss of equity. Higher exuberance ratios should result in an increase in the discount rate, which results in lower valuations as measured by the formula net operating income dividend by the discount rate. It can also signal when to start reinvesting in real estate, because real estate prices are rising, and the ratios are relative low compared to income.Social implicationsThe graphical descriptive depictions seem to suggest that government intervention into the housing market while a spike is forming may not be possible due to the speed with which a spike forms and collapses. Expected income declines would cause the income ratios to change and signal that housing prices will start declining. Both the income and rental ratios in the US housing market have continued to increase since 2008.Originality/valueA consumer sentiment variable was added to the analysis. Prior researchers have suggested adding a consumer sentiment explanatory variable to the model. The results generated for this variable were counterintuitive. The Federal Housing Finance Agency (FHFA) price index results signaled a change during a different year than when the S&P/Case–Shiller Home Price Index is used. Many prior studies used the FHFA price index. They emphasized regulatory issues associated with changing exuberance ratio levels. This case study applies these ideas to measure relative increases in risk, which should impact the discount rate used to estimate the intrinsic value of a residential property.

Predatory Inclusion

2019 ◽  
pp. 253-262
Author(s):  
Keeanga-Yamahtta Taylor
Keyword(s):  
African Americans ◽  
Real Estate ◽  
Housing Market ◽  
Black People ◽  
Home Ownership ◽  
Rental Market ◽  
American Society ◽  
The U.S

Homeownership in the U.S. is often touted as a means to escape poverty, build wealth, and fully participate in American society. However, racism in the broader American society ultimately resulted in a racist housing market that excludes Black people from homeownership and depresses the value of property inhabited by African Americans. The perception that Black buyers are risky has continued to fuel predatory practices in real estate. The author notes that African Americans should not be limited to the rental market because of inequality in the housing market. Instead, she suggests people should question American society, a society in which full citizenship is reliant upon home ownership.

scholarly journals 3D Modeling of Discontinuity in the Spatial Distribution of Apartment Prices Using Voronoi Diagrams

2020 ◽  
Vol 12 (2) ◽  
pp. 229 ◽  
Author(s):  
Mirosław Bełej ◽  
Marta Figurska
Keyword(s):  
Spatial Distribution ◽  
Real Estate ◽  
Housing Market ◽  
3D Modeling ◽  
3D Visualization ◽  
Voronoi Diagrams ◽  
Capital City ◽  
Data Sets ◽  
Spatial Continuity ◽  
Real Estate Prices

An immanent feature of the housing market is a large spatial dispersion of real estate prices along with their simultaneous high stratification. Application of classic methods of data interpolation results in an excessive simplification of the outcome because of a conversion of the dispersed data sets into areas of spatial continuity by reducing the above-average real estate prices. The main aim of the article was to search for spatial discontinuities of real estate prices’ distribution with 3D modeling using Voronoi diagrams as a method of irregular division of this space. Used methods of geospatial analyses with GIS tools enabled to identify clusters of high housing market activity and to avoid an excessive generalization of data resulting from the reduction of the above-average real estate prices. The research was conducted for over 7000 real estate transactions in years 2010–2017 in Olsztyn, the capital city of Warmia and Mazury in Poland, resulting in a 3D visualization of real estate prices for the chosen market, including the discontinuity in their spatial distribution.

The housing demand analysis and prediction of the real estate based on the AWGM (1, N) model

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Xin Xiong ◽  
Huan Guo ◽  
Xi Hu
Keyword(s):  
Real Estate ◽  
Housing Market ◽  
Housing Demand ◽  
Grey Model ◽  
Content Type ◽  
First Order ◽  
Model Based ◽  
Per Capita ◽  
Improved Model ◽  
Grey Relational

PurposeThe purpose of this paper is to seek to drive the modernization of the entire national economy and maintain in the long-term stability of the whole society; this paper proposes an improved model based on the first-order multivariable grey model [GM (1, N) model] for predicting the housing demand and solving the housing demand problem.Design/methodology/approachThis paper proposes an improved model based on the first-order multivariable grey model [GM (1, N) model] for predicting the housing demand and solving the housing demand problem. First, a novel variable SW evaluation algorithm is proposed based on the sensitivity analysis, and then the grey relational analysis (GRA) algorithm is utilized to select influencing factors of the commodity housing market. Finally, the AWGM (1, N) model is established to predict the housing demand.FindingsThis paper selects seven factors to predict the housing demand and find out the order of grey relational ranked from large to small: the completed area of the commodity housing> the per capita housing area> the one-year lending rate> the nonagricultural population > GDP > average price of the commodity housing > per capita disposable income.Practical implicationsThe model constructed in the paper can be effectively applied to the analysis and prediction of Chinese real estate market scientifically and reasonably.Originality/valueThe factors of the commodity housing market in Wuhan are considered as an example to analyze the sales area of the commodity housing from 2015 to 2017 and predict its trend from 2018 to 2019. The comparison between demand for the commodity housing actual value and one for model predicted value is capability to verify the effectiveness of the authors’ proposed algorithm.

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