scholarly journals Applying Comparable Sales Method to the Automated Estimation of Real Estate Prices

2020 ◽  
Vol 12 (14) ◽  
pp. 5679 ◽  
Author(s):  
Yunjong Kim ◽  
Seungwoo Choi ◽  
Mun Yong Yi
Keyword(s):  
Real Estate ◽  
Traditional Approach ◽  
Housing Prices ◽  
Capital City ◽  
Housing Type ◽  
Residential Housing ◽  
Machine Learning Approach ◽  
Price Estimation ◽  
Real Estate Prices ◽  
Current Price

In this paper, we propose a novel procedure designed to apply comparable sales method to the automated price estimation of real estates, in particular, that of apartments. Apartments are the most popular residential housing type in Korea. The price of a single apartment is influenced by many factors, making it hard to estimate accurately. Moreover, as an apartment is purchased for living, with a sizable amount of money, it is mostly traded infrequently. Thus, its past transaction price may not be particularly helpful to the estimation after a certain period of time. For these reasons, the up-to-date price of an apartment is commonly estimated by certified appraisers, who typically rely on comparable sales method (CSM). CSM requires comparable properties to be identified and used as references in estimating the current price of the property in question. In this research, we develop a procedure to systematically apply this procedure to the automated estimation of apartment prices and assess its applicability using nine years’ real transaction data from the capital city and the most-populated province in South Korea and multiple scenarios designed to reflect the conditions of low and high fluctuations of housing prices. The results from extensive evaluations show that the proposed approach is superior to the traditional approach of relying on real estate professionals and also to the baseline machine learning approach.

scholarly journals Evaluation of the Applicability of Statistical Methods in Studies on Price Dynamics on the Real Estate Market

2013 ◽  
Vol 21 (1) ◽  
pp. 49-58 ◽  
Author(s):  
Sebastian Kokot ◽  
Marcin Bas
Keyword(s):  
Real Estate ◽  
Traditional Approach ◽  
Real Estate Market ◽  
Price Dynamics ◽  
The Real ◽  
The Real Estate ◽  
Transaction Prices ◽  
Time Period ◽  
The Real Estate Market ◽  
Real Estate Prices

Abstract The specific character of the real estate market is the reason why observations of transaction prices seen as statistical variables are taken in a non-standard way. In the traditional approach each time period or specific moments of time are attributed with one observation of a studied variable per one object. In the case of the real estate market, this is not possible since transactions relate to different objects, i.e., properties, and occur at irregular, or even random, moments. This is why traditional methods used to examine the dynamics of economic phenomena must be adapted to specific conditions on the real estate market. Keeping that in mind, the aim of this paper is to adapt classical statistical examination methods of dynamics to specific conditions of the real estate market followed by the actual examination of the dynamics of real estate prices in three sub-segments of the housing market in Szczecin. On its basis, the authors evaluate various methods of examining real estate price dynamics in terms of their applicability in real estate appraisal procedures and, in a broader perspective, present characteristic phenomena that can be observed on the real estate market.

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.

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.

Real effects of real estate: evidence from unemployment rates

2020 ◽  
Vol 37 (4) ◽  
pp. 605-623
Author(s):  
Can Dogan ◽  
John Can Topuz
Keyword(s):  
Real Estate ◽  
Housing Prices ◽  
Real Economy ◽  
Residential Real Estate ◽  
Content Type ◽  
Unemployment Rates ◽  
The Real ◽  
Real Estate Price ◽  
Real Effects ◽  
Real Estate Prices

Purpose This paper aims to investigate the relationship between residential real estate prices and unemployment rates at the Metropolitan Statistical Area (MSA) level. Design/methodology/approach This paper uses a long time-series of MSA-level quarterly data from 1990 to 2018. It uses an instrumental variable approach to estimate the effects of residential real estate prices on unemployment rates using the geography-based land constraints measure of Saiz (2010) as the instrument. Findings The results show that changes in residential real estate prices do not have a causal effect on unemployment rates in the same quarter. However, it takes 9-12 months for an increase (decrease) in real estate prices to decrease (increase) unemployment rates. This effect is significant during both pre- and post-financial crisis periods and robust to control for the economic characteristics of MSAs. Research limitations/implications This paper contributes to the emerging literature that studies the real effects of real estate. Particularly, the methodology and the findings can be used to investigate causal relationships between housing prices and small business development or economic growth. The findings are also of interest to policymakers and practitioners as they illustrate how and when real estate price shocks propagate to the real economy through unemployment rates. Practical implications This study’s findings have important implications for academics, policymakers and investors as they provide evidence of a snowball effect associated with shocks to real estate prices: increasing (decreasing) unemployment rates following a decrease (increase) in real estate prices exacerbates the real estate price movements and their economic consequences. Originality/value This paper analyzes a significantly longer period, from 1990 to 2018, than the existing literature. Additionally, it uses the MSA-level land unavailability measure of Saiz (2010) as an instrument to explore the effects of residential real estate prices on unemployment rates and when those effects are observed in the real economy.

scholarly journals COVID-19 Infects Real Estate Markets: Short and Mid-Run Effects on Housing Prices in Campania Region (Italy)

2020 ◽  
Vol 9 (7) ◽  
pp. 114 ◽  
Author(s):  
Vincenzo Del Giudice ◽  
Pierfrancesco De Paola ◽  
Francesco Paolo Del Giudice
Keyword(s):  
Real Estate ◽  
Housing Prices ◽  
Real Estate Market ◽  
Campania Region ◽  
Real Estate Markets ◽  
The Real ◽  
The Real Estate ◽  
Short Run ◽  
Predator Model ◽  
Real Estate Prices

The COVID-19 (also called “SARS-CoV-2”) pandemic is causing a dramatic reduction in consumption, with a further drop in prices and a decrease in workers’ per capita income. To this will be added an increase in unemployment, which will further depress consumption. The real estate market, as for other productive and commercial sectors, in the short and mid-run, will not tend to move independently from the context of the aforementioned economic variables. The effect of pandemics or health emergencies on housing markets is an unexplored topic in international literature. For this reason, firstly, the few specific studies found are reported and, by analogy, studies on the effects of terrorism attacks and natural disasters on real estate prices are examined too. Subsequently, beginning from the real estate dynamics and economic indicators of the Campania region before the COVID-19 emergency, the current COVID-19 scenario is defined (focusing on unemployment, personal and household income, real estate judicial execution, real estate dynamics). Finally, a real estate pricing model is developed, evaluating the short and mid-run COVID-19 effects on housing prices. To predict possible changes in the mid-run of real estate judicial execution and real estate dynamics, the economic model of Lotka–Volterra (also known as the “prey–predator” model) was applied. Results of the model indicate a housing prices drop of 4.16% in the short-run and 6.49% in the mid-run (late 2020–early 2021).

scholarly journals Evaluation of Residential Housing Prices on the Internet: Data Pitfalls

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-15
Author(s):  
Ming Li ◽  
Guojun Zhang ◽  
Yunliang Chen ◽  
Chunshan Zhou
Keyword(s):  
Real Estate ◽  
Central City ◽  
Housing Prices ◽  
Housing Price ◽  
Data Sources ◽  
City Centre ◽  
The Internet ◽  
Residential Housing ◽  
For Profit ◽  
Comparison Results

Many studies have used housing prices on the Internet real estate information platforms as data sources, but platforms differ in the nature and quality of the data they release. However, few studies have analysed these differences or their effect on research. In this study, second-hand neighbourhood housing prices and information on five online real estate information platforms in Guangzhou, China, were comparatively analysed and the performance of neighbourhoods’ raw information from four for-profit online real estate information platforms was evaluated by applying the same housing price model. The comparison results show that the official second-hand residential housing prices at city and district level are generally lower than those issued on four for-profit real estate websites. The same second-hand neighbourhood housing prices are similar across each of the four for-profit real estate websites due to cross-referencing among real estate websites. The differences of housing prices in the central city area are significantly fewer than those in the periphery. The variation of each neighbourhood’s housing prices on each website decreases gradually from the city centre to the periphery, but the relative variation stays stable. The results of the four hedonic models have some inconsistencies with other studies’ findings, demonstrating that errors exist in raw information on neighbourhoods taken from Internet platforms. These results remind researchers to choose housing price data sources cautiously and that raw information on neighbourhoods from Internet platforms should be appropriately cleaned.

Housing Market Hedonic Price Study Based on Boosting Regression Tree

2017 ◽  
Vol 21 (6) ◽  
pp. 1040-1047 ◽  
Author(s):  
Guangtong Gu ◽  
Bing Xu ◽  
◽  
◽  
Keyword(s):  
Real Estate ◽  
Housing Prices ◽  
Regression Tree ◽  
Gradient Boosting ◽  
Hedonic Price ◽  
Tree Model ◽  
Policy Makers ◽  
Real Estate Markets ◽  
Architectural Features ◽  
Real Estate Prices

Based on the purchase price data of new real estate markets three cities in China, Beijing, Shanghai, and Guangzhou, including architectural features, neighborhood property features, and location features, in this study a boosting regression tree model was built to study the factors and the influence path of housing prices from the microcosmic perspective. First, a classical hedonic price model was constructed to analyze and compare the significant effect factors on housing prices in the market segments of the three cities. Second, the gradient boosting regression tree method that is proposed in this paper was applied to the three markets in combination to analyze the influence paths and factors and the importance of the type of housing hedonic price. The influence paths of housing hedonic prices and decision tree rules are visualized. The significant housing features are effectively extracted. Finally, we present three main conclusions and several suggestions for policy makers to improve urban functions while stabilizing real estate prices.

scholarly journals Guyana

2020 ◽  
Vol 20 (20) ◽  
Author(s):  
Julian Chow
Keyword(s):  
Commercial Banks ◽  
Housing Prices ◽  
Bank Lending ◽  
Capital City ◽  
Household Debt ◽  
Housing Sector ◽  
Residential Real Estate ◽  
Stages Of Growth ◽  
Data Limitations ◽  
Real Estate Prices

Guyana’s residential real estate prices have been rising, particularly in the capital city Georgetown, following the discovery of oil in 2015. In line with the growing demand for housing, commercial banks’ housing loans have increased, prompting higher household debt. This paper presents two analyses which suggest that housing prices in Georgetown and banks’ lending to the housing sector appear to be in their early stages of growth. However, given the data limitations and caveats that underpin the analyses, the findings could also indicate early signals of possible risks. Further data collection would support surveillance and deeper studies. At the same time, enhancing prudential measures would help safeguard financial and macroeconomic stability. These include strengthening the monitoring of the housing market, bank lending practices and household debt, as well as fortifying the macroprudential framework, including with more effective toolkits for early intervention.

scholarly journals Hedonic Analysis of Housing Prices and Development in Kaunas: Heritage Aspect

2019 ◽  
Vol 75 (2) ◽  
pp. 15-27
Author(s):  
Kastytis Rudokas ◽  
Mantas Landauskas ◽  
Odeta Viliūnienė ◽  
Indrė Gražulevičiūtė - Vileniškė
Keyword(s):  
Real Estate ◽  
Urban Areas ◽  
Housing Prices ◽  
Positive Influence ◽  
Hedonic Price ◽  
The Real ◽  
The Real Estate ◽  
Heritage Buildings ◽  
New Construction ◽  
Real Estate Prices

The urban economists have stressed the importance of various amenities for the attractiveness of urban areas for residents and businesses and built cultural heritage can be considered as one of such amenities, the benefits of which should not be overlooked. This research was aimed to analyze the influence of heritage aspect including the heritage status or features of the building and the historic built environment in general on the real estate prices and development in Kaunas using hedonic price method. Two sets of data were collected for the analysis - general, including heritage buildings and including new construction since 2013. The research has demonstrated that heritage status and the year of construction (as older buildings can be considered having heritage features) have no significant positive influence on the real estate prices. Meanwhile, the location, heritage context and the architectural distinctiveness of new architecture have a direct influence on the real estate prices. The heritage context correlates with architectural quality of new construction as well. This reveals the benefits of heritage context both for the real estate developers and households; however, the study shows the unemployed social-economic potential of historic buildings as generators and maintainers of heritage context.

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