Modeling Owner-Occupied Single-Family House Values in the City of Milwaukee: A Geographically Weighted Regression Approach

2007 ◽  
Vol 44 (3) ◽  
pp. 267-282 ◽  
Author(s):  
Danlin Yu
Keyword(s):  
Geographically Weighted Regression ◽  
Weighted Regression ◽  
Single Family ◽  
House Values ◽  
Regression Approach ◽  
The City ◽  
Family House

Number of flood disaster estimation in Indonesia using local linear and geographically weighted regression approach

2021 ◽  
Author(s):  
M. Fariz Fadillah Mardianto ◽  
Sediono ◽  
Novia Anggita Aprilianti ◽  
Belindha Ayu Ardhani ◽  
Rizka Firdaus Rahmadina ◽  
...  
Keyword(s):  
Geographically Weighted Regression ◽  
Flood Disaster ◽  
Weighted Regression ◽  
Local Linear ◽  
Regression Approach

Assessing the Effect of Mansionization on Suburban Single-Family House Sales Prices

2019 ◽  
pp. 0739456X1983315
Author(s):  
Suzanne Lanyi Charles
Keyword(s):  
Random Effects ◽  
Mixed Effects ◽  
Mixed Effects Model ◽  
Single Family ◽  
Socioeconomic Characteristics ◽  
House Values ◽  
Crossed Random Effects ◽  
Family House

Using observed single-family house sales in the inner-ring suburbs of Chicago from 2010 through 2017, this paper uses a multilevel mixed-effects model with crossed random effects to estimate the effect that millennium mansions—new, large single-family houses—have on the sales prices of nearby single-family houses. Controlling for property, sale timing, and surrounding neighborhood socioeconomic characteristics, the study finds that mansionization is associated with an increase in the sales prices of neighboring houses. Long-term residents of a neighborhood undergoing mansionization should not fear a decrease in their house values; however, decreases in neighborhood affordability may result in exclusionary displacement.

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 Housing Preferences of Seniors and Pre-Senior Citizens in Poland—A Case Study

2020 ◽  
Vol 12 (11) ◽  
pp. 4599 ◽  
Author(s):  
Anna Jancz ◽  
Radoslaw Trojanek
Keyword(s):  
Senior Citizens ◽  
The Other ◽  
Single Family ◽  
City Center ◽  
Housing Preferences ◽  
Large Cities ◽  
Family Housing ◽  
The City ◽  
Family House

This article identifies and compares the housing preferences of seniors and pre-senior citizens in Poland. In addition, the attitude of residents of large cities in the Wielkopolskie Voivodeship towards senior citizens’ housing was determined. Surveys were conducted in the two largest cities of this region. The influence of the potential behaviors of this group of society on the development of housing was also examined. Results showed that differentiation of housing preferences was visible primarily when choosing the type of development and size of the dwelling. Seniors preferred smaller units in multi-family housing construction. Pre-senior citizens, on the other hand, were more likely to think about living in a single-family house. The location of a new dwelling was also important. Seniors, more often than people aged 50–59, chose a location in the city center. Pre-senior citizens, in contrast, more often decided to live in a rural area or outside the city center. Moreover, the attitude of seniors towards senior citizens’ housing is undecided, which may indicate that many people may change their housing preferences in the future and decide to move.

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