scholarly journals Augmenting Geostatistics with Matrix Factorization: A Case Study for House Price Estimation

2020 ◽  
Vol 9 (5) ◽  
pp. 288
Author(s):  
Aisha Sikder ◽  
Andreas Züfle
Keyword(s):  
Matrix Factorization ◽  
Geographically Weighted Regression ◽  
House Prices ◽  
Housing Prices ◽  
House Price ◽  
Weighted Regression ◽  
Regression Kriging ◽  
Truncated Svd ◽  
Latent Features ◽  
Kriging Approach

Singular value decomposition (SVD) is ubiquitously used in recommendation systems to estimate and predict values based on latent features obtained through matrix factorization. But, oblivious of location information, SVD has limitations in predicting variables that have strong spatial autocorrelation, such as housing prices which strongly depend on spatial properties such as the neighborhood and school districts. In this work, we build an algorithm that integrates the latent feature learning capabilities of truncated SVD with kriging, which is called SVD-Regression Kriging (SVD-RK). In doing so, we address the problem of modeling and predicting spatially autocorrelated data for recommender engines using real estate housing prices by integrating spatial statistics. We also show that SVD-RK outperforms purely latent features based solutions as well as purely spatial approaches like Geographically Weighted Regression (GWR). Our proposed algorithm, SVD-RK, integrates the results of truncated SVD as an independent variable into a regression kriging approach. We show experimentally, that latent house price patterns learned using SVD are able to improve house price predictions of ordinary kriging in areas where house prices fluctuate locally. For areas where house prices are strongly spatially autocorrelated, evident by a house pricing variogram showing that the data can be mostly explained by spatial information only, we propose to feed the results of SVD into a geographically weighted regression model to outperform the orginary kriging approach.

scholarly journals The role and significance of planning in the determination of house prices in Australia: Recent policy debates

2021 ◽  
pp. 0308518X2198894
Author(s):  
Peter Phibbs ◽  
Nicole Gurran
Keyword(s):  
Land Use ◽  
Housing Market ◽  
Land Use Planning ◽  
House Prices ◽  
Housing Prices ◽  
House Price ◽  
Evidence Base ◽  
Urban Housing ◽  
Price Inflation ◽  
Depth Analysis

On the world stage, Australian cities have been punching above their weight in global indexes of housing prices, sparking heated debates about the causes of and remedies for, sustained house price inflation. This paper examines the evidence base underpinning such debates, and the policy claims made by key commentators and stakeholders. With reference to the wider context of Australia’s housing market over a 20 year period, as well as an in depth analysis of a research paper by Australia’s central Reserve Bank, we show how economic theories commonly position land use planning as a primary driver of new supply constraints but overlook other explanations for housing market behavior. In doing so, we offer an alternative understanding of urban housing markets and land use planning interventions as a basis for more effective policy intervention in Australian and other world cities.

scholarly journals Assessing the impacts of Airbnb listings on London house prices

2021 ◽  
pp. 239980832110018
Author(s):  
James Todd ◽  
Anwar Musah ◽  
James Cheshire
Keyword(s):  
Random Effects ◽  
Rapid Growth ◽  
House Prices ◽  
Housing Prices ◽  
House Price ◽  
Positive Association ◽  
Sharing Economy ◽  
Local Context ◽  
Fixed And Random Effects ◽  
The World

Over the course of the last decade, sharing economy platforms have experienced significant growth within cities around the world. Airbnb, which is one of the largest and best-known platforms, provides the focus for this paper and offers a service that allows users to rent properties or spare rooms to guests. Its rapid growth has led to a growing discourse around the consequences of Airbnb rentals within the local context. The research within this paper focuses on determining impact on local housing prices within the inner London boroughs by constructing a longitudinal panel dataset, on which a fixed and random effects regression was conducted. The results indicate that there is a significant and modest positive association between the frequency of Airbnb and the house price per square metre in these boroughs.

Second-hand housing batch evaluation model of zhengzhou city based on big data and MGWR model

2021 ◽  
pp. 1-20
Author(s):  
Chaojie Liu ◽  
Jie Lu ◽  
Wenjing Fu ◽  
Zhuoyi Zhou
Keyword(s):  
Real Estate ◽  
Geographically Weighted Regression ◽  
Euclidean Distance ◽  
Evaluation Model ◽  
Housing Prices ◽  
Information Criterion ◽  
Access Point ◽  
Least Square ◽  
Weighted Regression ◽  
Spatial Error

How to better evaluate the value of urban real estate is a major issue in the reform of real estate tax system. So the establishment of an accurate and efficient housing batch evaluation model is crucial in evaluating the value of housing. In this paper the second-hand housing transaction data of Zhengzhou City from 2010 to 2019 was used to model housing prices and explanatory variables by using models of Ordinary Least Square (OLS), Spatial Error Model (SEM), Geographically Weighted Regression (GWR), Geographically and Temporally Weighted Regression (GTWR), and Multiscale Geographically Weighted Regression (MGWR). And a correction method of Barrier Line and Access Point (BLAAP) was constructed, and compared with three correction methods previously studied: Buffer Area (BA), Euclidean Distance (ED), and Non-Euclidean Distance, Travel Distance (ND, TT). The results showed: The fitting degree of GWR, MGWR and GTWR by BLAAP was 0.03–0.07 higher than by ND. The fitting degree of MGWR was the highest (0.883) by BLAAP but the smallest by Akaike Information Criterion (AIC), and 88.3% of second-hand housing data could be well interpreted by the model.

Effects of different sampling densities on geographically weighted regression kriging for predicting soil organic carbon

2017 ◽  
Vol 20 ◽  
pp. 76-91 ◽  
Author(s):  
Huichun Ye ◽  
Wenjiang Huang ◽  
Shanyu Huang ◽  
Yuanfang Huang ◽  
Shiwen Zhang ◽  
...  
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Geographically Weighted Regression ◽  
Weighted Regression ◽  
Regression Kriging

scholarly journals Liquidity, interest rates and house prices in the euro area: a DSGE analysis

2016 ◽  
Vol 9 (1) ◽  
pp. 4-25 ◽  
Author(s):  
Margarita Rubio ◽  
José A. Carrasco-Gallego
Keyword(s):  
Interest Rates ◽  
Monetary Union ◽  
Euro Area ◽  
House Prices ◽  
Housing Prices ◽  
House Price ◽  
Dsge Model ◽  
Content Type ◽  
The Core ◽  
Dynamic Stochastic

Purpose This study aims to build a two-country monetary union dynamic stochastic general equilibrium (DSGE) model with housing to assess how different shocks contributed to the increase in housing prices and credit in the European Economic and Monetary Union. One of the countries is calibrated to represent the core group in the euro area, while the other one corresponds to the periphery. Design/methodology/approach In this paper, the authors explore how a liquidity shock (or a decrease in the interest rate) affects house prices and the real economy through the asset price and the collateral channel. Then, they analyze how a house price shock in the periphery and a technology shock in the core countries are transmitted to both economies. Findings The authors find that a combination of an increase in liquidity in the euro area coming from the common monetary policy, together with asymmetric house price and technology shocks, contributed to an increase in house prices in the euro area and a stronger credit growth in the peripheral economies. Originality/value This paper represents the theoretical counterpart to empirical studies that show, through macroeconometric models, the interrelation between liquidity and other shocks with house prices. Using a DSGE model with housing, the authors disentangle the mechanisms behind these empirical findings.

Fast Geographically Weighted Regression (FastGWR): a scalable algorithm to investigate spatial process heterogeneity in millions of observations

2019 ◽  
Vol 33 (1) ◽  
pp. 155-175 ◽  
Author(s):  
Li ◽  
Fotheringham ◽  
Li ◽  
Oshan
Keyword(s):  
Los Angeles ◽  
Open Source ◽  
Geographically Weighted Regression ◽  
Message Passing Interface ◽  
House Price ◽  
Weighted Regression ◽  
Spatial Process ◽  
Parameter Estimates ◽  
Single Family ◽  
Scalable Algorithm

Geographically Weighted Regression (GWR) is a widely used tool for exploring spatial heterogeneity of processes over geographic space. GWR computes location-specific parameter estimates, which makes its calibration process computationally intensive. The maximum number of data points that can be handled by current open-source GWR software is approximately 15,000 observations on a standard desktop. In the era of big data, this places a severe limitation on the use of GWR. To overcome this limitation, we propose a highly scalable, open-source FastGWR implementation based on Python and the Message Passing Interface (MPI) that scales to the order of millions of observations. FastGWR optimizes memory usage along with parallelization to boost performance significantly. To illustrate the performance of FastGWR, a hedonic house price model is calibrated on approximately 1.3 million single-family residential properties from a Zillow dataset for the city of Los Angeles, which is the first effort to apply GWR to a dataset of this size. The results show that FastGWR scales linearly as the number of cores within the High-Performance Computing (HPC) environment increases. It also outperforms currently available open-sourced GWR software packages with drastic speed reductions – up to thousands of times faster – on a standard desktop.

scholarly journals Spatial Analysis of Housing Prices and Market Activity with the Geographically Weighted Regression

2020 ◽  
Vol 9 (6) ◽  
pp. 380
Author(s):  
Radosław Cellmer ◽  
Aneta Cichulska ◽  
Mirosław Bełej
Keyword(s):  
Spatial Heterogeneity ◽  
Housing Market ◽  
Spatial Data ◽  
Geographically Weighted Regression ◽  
Housing Prices ◽  
Spatial Diversity ◽  
Spatial Data Analysis ◽  
Weighted Regression ◽  
Market Activity ◽  
Administrative Units

The main part of the study will be to demonstrate that models taking into account spatial heterogeneity (Geographically Weighted Regression and Mixed Geographically Weighted Regression) which reproduce housing market determinants better reflect market relationships than conventional regression models. The spatial heterogeneity of the housing market determinants results in the spatial diversity of the market activity, as well as of real estate prices and values. The main aim of the study was to analyse an effect of these socio-demographic and environmental factors on average housing property prices and on the number of transactions in a spatial approach. In previous research conducted on a national scale, usually all variables were treated in a similar way, i.e., as global or local variables. During the research, an attempt was also made to answer the question of which of the variables adopted for analysis have a local impact on prices and market activity, and which are global. The study was conducted in Poland and used data from the year 2018 on 380 counties (Local Administrative Units). The study showed that determinants both for average prices and for the housing market activity show spatial autocorrelation with high–high and low–low cluster groups. Owing to these models, it was possible to draw specific conclusions on local determinants of flat prices and the market activity in Poland. The study findings have confirmed that they are an extremely effective tool for spatial data analysis.

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