scholarly journals Valuing Rural Recreation Amenities: Hedonic Prices for Vacation Rental Houses at Deep Creek Lake, Maryland

2010 ◽  
Vol 39 (3) ◽  
pp. 485-504 ◽  
Author(s):  
Jon P. Nelson
Keyword(s):  
Real Estate ◽  
Least Squares ◽  
Spatial Correlation ◽  
Ordinary Least Squares ◽  
Hedonic Prices ◽  
Spatial Error ◽  
Spatial Lag ◽  
Implicit Prices ◽  
Real Estate Management ◽  
House Size

Hedonic prices are estimated for summer and winter rentals for vacation houses located near a lake and ski-golf resort in rural western Maryland. Regressions for weekly rents are conditioned on house size, quality, and recreation features including lakefront proximity and ski-slope access. Percentage effects and marginal implicit prices indicate that access to recreation is reflected importantly in rental offers. Evaluated at the means, lakefront locations command a premium of $1,100–1,200 per week, and the premium for ski-slope access is $500–600 per week. Unit recreation values are about $18 per person per day for a lakefront location with a private dock and $7 per person per day for a ski-slope location. There are small differences in the unit values for three real estate management agencies. Although there is evidence of spatial correlation in ordinary least squares residuals, estimation of spatial-lag and spatial-error models does not yield substantial changes in the empirical results.

scholarly journals The Impact of Inter-City Traffic Restriction on COVID-19 Transmission from Spatial Econometric Perspective

2021 ◽  
Vol 33 (5) ◽  
pp. 705-716
Author(s):  
Xijin Lu ◽  
Changxi Ma
Keyword(s):  
Spatial Correlation ◽  
Goodness Of Fit ◽  
Ordinary Least Squares ◽  
Hubei Province ◽  
Error Model ◽  
Moran’S I ◽  
Spatial Error ◽  
Moran's I ◽  
Spatial Lag ◽  
The Impact

The aim of this paper is to conduct a spatial correlation study of virus transmission in the Hubei province, China. The number of confirmed COVID-19 cases released by the National Health and Construction Commission, the traffic flow data provided by Baidu migration, and the current situation of Wuhan intercity traffic were collected. The Moran’s I test shows that there is a positive spatial correlation between the 17 cities in the Hubei province. The result of Moran’s I test also shows that four different policies to restrict inter-city traffic can be issued for the four types of cities. The ordinary least squares regression, spatial lag model, spatial error model, and spatial lag error model were built. Based on the analysis of the spatial lag error model, whose goodness of fit is the highest among the four models, it can be concluded that the speed of COVID-19 spread within a certain region is not only related to the current infection itself but also associated with the scale of the infection in the surrounding area. Thus, the spill-over effect of the COVID-19 is also presented. This paper bridges inter-city traffic and spatial economics, provides a theoretical contribution, and verifies the necessity of a lockdown from an empirical point of view.

Algorithm of real estate mass appraisal with inequality restricted least squares (IRLS) estimation

2020 ◽  
Vol 13 (2) ◽  
pp. 161-179
Author(s):  
Mariusz Doszyń
Keyword(s):  
Real Estate ◽  
Least Squares ◽  
Econometric Model ◽  
A Priori ◽  
Ordinary Least Squares ◽  
Real Estate Market ◽  
Negative Consequences ◽  
Content Type ◽  
Low Efficiency ◽  
The Impact

Purpose The purpose of this paper is to present an algorithm of real estate mass appraisal in which the impact of attributes (real estate features) is estimated by inequality restricted least squares (IRLS) model. Design/methodology/approach This paper presents the algorithm of real estate mass appraisal, which was also presented in the form of an econometric model. Vital problem related to econometric models of mass appraisal is multicollinearity. In this paper, a priori knowledge about parameters is used by imposing restrictions in the form of inequalities. IRLS model is therefore used to limit negative consequences of multicollinearity. In ordinary least squares (OLS) models, estimator variances might be inflated by multicollinearity, which could lead to wrong signs of estimates. In IRLS models, estimators efficiency is higher (estimator variances are lower), which could result in better appraisals. Findings The final effect of the analysis is a vector of the impact of real estate attributes on their value in the mass appraisal algorithm. After making expert corrections, the algorithm was used to evaluate 318 properties from the test set. Valuation errors were also discussed. Originality/value Restrictions in the form of inequalities were imposed on the parameters of the econometric model, ensuring the non-negativity and monotonicity of real estate attribute impact. In case of real estate, variables are usually correlated. OLS estimators are then inflated and inefficient. Imposing restrictions in form of inequalities could improve results because IRLS estimators are more efficient. In the case of results inconsistent with theoretical assumptions, the real estate mass appraisal algorithm enables having the obtained results adjusted by an expert. This can be important for low quality databases, which is often the case in underdeveloped real estate markets. Another reason for expert correction may be the low efficiency of a given real estate market.

scholarly journals The value of rural landscape in Aquitania (Colombia): application of spatial hedonic models in real estate analysis

2016 ◽  
Vol 12 (76) ◽  
pp. 155
Author(s):  
Ana Milena Plata Fajardo ◽  
Julio Cañón ◽  
Raffaele Lafortezza
Keyword(s):  
Real Estate ◽  
Structural Characteristics ◽  
Economic Value ◽  
Ordinary Least Squares ◽  
Rural Landscape ◽  
Environmental Amenities ◽  
Spatial Error ◽  
Spatial Lag Model ◽  
Lag Model ◽  
Real Estate Economics

This study addresses the marginal economic value of environmental amenities, structural characteristics, neighborhood facilities, and accessibility on property in Aquitania - Colombia. Based on 400 assessed values of rural land property and on 21 characteristic variables of land amenities and facilities, the study compares three models: Ordinary Least Squares (ols), Spatial Lag Model (slm), and Spatial Error Model (sem). Results show that both slm and sem outperformed ols in identifying the significance of real estate attributes. Results shows that farmers value environmental amenities more than other attributes, being implicit the greater value of cattle over agriculture (onion) in land use. These models may help to support decisions in rural real estate economics.

Spatial autoregressive analysis of nationwide street network patterns with global open data

2021 ◽  
pp. 239980832098784
Author(s):  
Qi Zhou ◽  
Hao Lin ◽  
Junya Bao
Keyword(s):  
Open Data ◽  
Ordinary Least Squares ◽  
Street Network ◽  
Spatial Error ◽  
Spatial Lag ◽  
Spatial Lag Model ◽  
Physical Form ◽  
Lag Model ◽  
Spatial Autoregressive ◽  
Network Patterns

The study of street network patterns is beneficial in understanding the layout or physical form of a city. Many studies have analyzed street network patterns, but the similarity and/or difference of street network patterns across a country or region are rarely quantitatively understood. To fill this gap, this research proposes a quantitative analysis of street network patterns nationwide. Specifically, the street network patterns across a country or region were first mapped, and then the relationship between such patterns and various landscape factors (calculated based on global open data) was quantitatively investigated by employing three regression models (ordinary least squares, spatial lag model, and spatial error model). Not only the whole region of China but also its subregions were used as study areas, which involved a total of 362 prefecture-level cities and 2081 built-up areas for analysis. Results showed that (1) similar street network patterns are spatially aggregated; (2) a number of factors, including both land-cover and terrain factors, are found to be significantly correlated with street network patterns; and (3) the spatial lag model is preferred in most of the application scenarios. Not only the analytical method and data can be applied to other countries and regions but also these findings are useful for understanding street network patterns and their associated urban forms in a country or region.

scholarly journals EPC Labels and Building Features: Spatial Implications over Housing Prices

2021 ◽  
Vol 13 (5) ◽  
pp. 2838
Author(s):  
Alice Barreca ◽  
Elena Fregonara ◽  
Diana Rolando
Keyword(s):  
Real Estate ◽  
Spatial Data ◽  
Housing Stock ◽  
Housing Prices ◽  
Energy Performance ◽  
Ordinary Least Squares ◽  
Real Estate Market ◽  
Hedonic Price ◽  
Spatial Error ◽  
The Impact

The influence of building or dwelling energy performance on the real estate market dynamics and pricing processes is deeply explored, due to the fact that energy efficiency improvement is one of the fundamental reasons for retrofitting the existing housing stock. Nevertheless, the joint effect produced by the building energy performance and the architectural, typological, and physical-technical attributes seems poorly studied. Thus, the aim of this work is to investigate the influence of both energy performance and diverse features on property prices, by performing spatial analyses on a sample of housing properties listed on Turin’s real estate market and on different sub-samples. In particular, Exploratory Spatial Data Analyses (ESDA) statistics, standard hedonic price models (Ordinary Least Squares—OLS) and Spatial Error Models (SEM) are firstly applied on the whole data sample, and then on three different sub-samples: two territorial clusters and a sub-sample representative of the most energy inefficient buildings constructed between 1946 and 1990. Results demonstrate that Energy Performance Certificate (EPC) labels are gaining power in influencing price variations, contrary to the empirical evidence that emerged in some previous studies. Furthermore, the presence of the spatial effects reveals that the impact of energy attributes changes in different sub-markets and thus has to be spatially analysed.

scholarly journals DO EUROPEAN REAL ESTATE STOCKS HEDGE INFLATION? EVIDENCE FROM DEVELOPED AND EMERGING MARKETS

2014 ◽  
Vol 18 (2) ◽  
pp. 178-197 ◽  
Author(s):  
Chyi Lin Lee ◽  
Ming-Long Lee
Keyword(s):  
Real Estate ◽  
Emerging Markets ◽  
Least Squares ◽  
Ordinary Least Squares ◽  
The Real Estate ◽  
Empirical Results ◽  
Expected Inflation ◽  
Long Run ◽  
Short Run ◽  
Inflation Hedging

This study examines the inflation-hedging properties of European real estate stocks in developed and emerging markets over 1990 to 2011. The Fama and Schwert model and a dynamic ordinary least squares (DOLS) regression were employed to study the inflation-hedging characteristics of European real estate stocks over the short run and long run. The empirical results show little inflationhedging ability of European real estate stocks over the short run. Over the long run, developed real estate stocks provide a positive inflation hedge against expected inflation, while no similar evidence is found in the emerging markets. The findings suggest that the inflation-hedging properties of real estate stocks are related to the institutional involvement in the real estate stock markets. The finding could have profound implications to institutional investors.

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