NON-STATIONARY SEMIVARIOGRAM ANALYSIS USING REAL ESTATE TRANSACTION DATA

2010 ◽  
Keyword(s):  
Real Estate ◽  
Transaction Data ◽  
Semivariogram Analysis

scholarly journals Changes On the Housing Market of the Downtown Area in Selected Polish Cities

2017 ◽  
Vol 25 (2) ◽  
pp. 79-90
Author(s):  
Izabela Rącka ◽  
Sławomir Palicki ◽  
Małgorzata Krajewska ◽  
Kinga Szopińska ◽  
Olgierd Kempa
Keyword(s):  
Real Estate ◽  
Economic Condition ◽  
Residential Real Estate ◽  
Real Estate Markets ◽  
Transaction Data ◽  
Main Research ◽  
The Real Estate ◽  
Downtown Area ◽  
The City ◽  
Transaction Price

Abstract Large Polish cities are currently dealing with an increasing significance of downtown areas, extending outside of the city centers (meaning the area directly surrounding the city square). The downtown alone seems to influence the fate of entire cities, facilitating their success or contributing to their failure. A good demographic, social and economic condition of a downtown, its positive image and the dynamic development of the part of the city perceived as the business and administration centre and a meeting place of residents and tourists, contribute to the image and potential of the whole city to a great extent. Changes in urban surroundings, the signs of which may be observed in spatial, aesthetic, architectural, urban-planning and socio-economic aspects, determine the functioning and condition of local real estate markets. Whether potential buyers consider the real estate attractive depends on the assessment of its significant features, of which transaction price is representative. The main research objective of the article is the identification, assessment and interpretation of differences in prices registered in the years 2009-2014 in downtown residential real estate markets. These considerations have been referred to analogical phenomena within the entire cities under examination. The detailed research objective is an attempt to explain the sources of individual reactions of the analyzed real estate markets in downtown areas. The cities under research include: Bydgoszcz, Kalisz, Toruń and Wrocław. The authors applied quantitative analysis (statistical, comparative) to transaction data registered in local residential real estate markets.

scholarly journals Does Climate Change Affect Real Estate Prices? Only If You Believe In It

2020 ◽  
Vol 33 (3) ◽  
pp. 1256-1295 ◽  
Author(s):  
Markus Baldauf ◽  
Lorenzo Garlappi ◽  
Constantine Yannelis
Keyword(s):  
Climate Change ◽  
Real Estate ◽  
Equilibrium Model ◽  
House Prices ◽  
Climate Risk ◽  
Housing Choice ◽  
Transaction Data ◽  
Long Run ◽  
Climate Change Risks ◽  
Real Estate Prices

Abstract This paper studies whether house prices reflect belief differences about climate change. We show that in an equilibrium model of housing choice in which agents derive utility from ownership in a neighborhood of similar agents, prices exhibit different elasticities to climate risk. We use comprehensive transaction data to relate prices to inundation projections of individual homes and measures of beliefs about climate change. We find that houses projected to be underwater in believer neighborhoods sell at a discount compared to houses in denier neighborhoods. Our results suggest that house prices reflect heterogeneity in beliefs about long-run climate change risks.

scholarly journals Using Machine Learning Models and Actual Transaction Data for Predicting Real Estate Prices

2020 ◽  
Vol 10 (17) ◽  
pp. 5832 ◽  
Author(s):  
Ping-Feng Pai ◽  
Wen-Chang Wang
Keyword(s):  
Machine Learning ◽  
Real Estate ◽  
Least Squares ◽  
Support Vector Regression ◽  
Support Vector ◽  
Learning Models ◽  
Transaction Data ◽  
Price Prediction ◽  
Real Estate Prices ◽  
Machine Learning Models

Real estate price prediction is crucial for the establishment of real estate policies and can help real estate owners and agents make informative decisions. The aim of this study is to employ actual transaction data and machine learning models to predict prices of real estate. The actual transaction data contain attributes and transaction prices of real estate that respectively serve as independent variables and dependent variables for machine learning models. The study employed four machine learning models-namely, least squares support vector regression (LSSVR), classification and regression tree (CART), general regression neural networks (GRNN), and backpropagation neural networks (BPNN), to forecast real estate prices. In addition, genetic algorithms were used to select parameters of machine learning models. Numerical results indicated that the least squares support vector regression outperforms the other three machine learning models in terms of forecasting accuracy. Furthermore, forecasting results generated by the least squares support vector regression are superior to previous related studies of real estate price prediction in terms of the average absolute percentage error. Thus, the machine learning-based model is a substantial and feasible way to forecast real estate prices, and the least squares support vector regression can provide relatively competitive and satisfactory results.

scholarly journals Novel Exploratory Spatiotemporal Analysis to Identify Sociospatial Patterns at Small Areas Using Property Transaction Data in Dublin

Land ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 566
Author(s):  
Hamidreza Rabiei-Dastjerdi ◽  
Gavin McArdle
Keyword(s):  
Real Estate ◽  
Urban Areas ◽  
Urban Economics ◽  
Real Estate Market ◽  
Space Time ◽  
Spatial Inequality ◽  
Residential Property ◽  
Residential Real Estate ◽  
Transaction Data ◽  
Small Areas

The residential real estate market is very important because most people’s wealth is in this sector, and it is an indicator of the economy. Real estate market data in general and market transaction data, in particular, are inherently spatiotemporal as each transaction has a location and time. Therefore, exploratory spatiotemporal methods can extract unique locational and temporal insight from property transaction data, but this type of data are usually unavailable or not sufficiently geocoded to implement spatiotemporal methods. In this article, exploratory spatiotemporal methods, including a space-time cube, were used to analyze the residential real estate market at small area scale in the Dublin Metropolitan Area over the last decade. The spatial patterns show that some neighborhoods are experiencing change, including gentrification and recent development. The extracted spatiotemporal patterns from the data show different urban areas have had varying responses during national and global crises such as the economic crisis in 2008–2011, the Brexit decision in 2016, and the COVID-19 pandemic. The study also suggests that Dublin is experiencing intraurban displacement of residential property transactions to the west of Dublin city, and we are predicting increasing spatial inequality and segregation in the future. The findings of this innovative and exploratory data-driven approach are supported by other work in the field regarding Dublin and other international cities. The article shows that the space-time cube can be used as complementary evidence for different fields of urban studies, urban planning, urban economics, real estate valuations, intraurban analytics, and monitoring sociospatial changes at small areas, and to understand residential property transactions in cities. Moreover, the exploratory spatiotemporal analyses of data have a high potential to highlight spatial structures of the city and relevant underlying processes. The value and necessity of open access to geocoded spatiotemporal property transaction data in social research are also highlighted.

scholarly journals Real estate listings and their usefulness for hedonic regressions

2021 ◽  
Author(s):  
Jens Kolbe ◽  
Rainer Schulz ◽  
Martin Wersing ◽  
Axel Werwatz
Keyword(s):  
Real Estate ◽  
Willingness To Pay ◽  
Price Index ◽  
Error Variance ◽  
Large Error ◽  
Data Sets ◽  
Transaction Data ◽  
Market Values ◽  
Research Areas ◽  
Implicit Prices

AbstractReal estate platforms provide a new source of data which has already been used as a substitute for transaction data in hedonic regression applications. This paper asks whether it is valid to do so in the established research areas of (1) willingness to pay estimation, (2) automated valuations, and (3) price index construction. It therefore compares listings and transaction data and regression results derived from them. We find that ask prices stochastically dominate sale prices, mainly because the composition of characteristics differs between the two data sets. But estimates of implicit prices also differ. As a result, willingness to pay estimates from listings data can be widely off when compared with estimates from transaction data. Listings data are not very useful to predict market values of individual houses either, as these predictions suffer from upward bias and large error variance. We find, however, that an ask price index complements a sale price index, as it is useful for nowcasting.

scholarly journals International Real Estate Review

2019 ◽  
Vol 22 (2) ◽  
pp. 131-167
Author(s):  
Mi Diao ◽  
◽  
Yi Fan ◽  
Tien Sing ◽  
◽  
...  
Keyword(s):  
Real Estate ◽  
Public Housing ◽  
Risk Averse ◽  
Ceteris Paribus ◽  
Transaction Data ◽  
Large Unit ◽  
The Core ◽  
Transaction Prices ◽  
Private Housing ◽  
The Government

The government of Singapore imposed two rounds of demand restrictions in 2010 and 2013, respectively, which prohibited private housing owners from concurrently owning both a private housing unit and a public housing flat. These restrictions curb speculative and investment activities, but do not deter public housing owners from upgrading to private housing. Using private housing transaction data between 2005 and 2015, we find that the demand shocks in 2010 and 2013 caused a significant reduction of 2.4% and 1.8% in the transaction prices of investors relative to those of the owners, respectively, ceteris paribus. Larger price declines are observed in investment sales in the submarkets, such as the core central region, and resale, moderate-to-high end, and large unit markets. The results show that when the housing market is volatile, risk averse investors are found, and owners move up the ¡§quality¡¨ curve by upgrading their home.

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