The impact of agricultural conservation easement on nearby house prices: Incorporating spatial autocorrelation and spatial heterogeneity

2016 ◽  
Vol 25 ◽  
pp. 78-93 ◽  
Author(s):  
James Yoo ◽  
Richard Ready
Keyword(s):  
Spatial Heterogeneity ◽  
Spatial Autocorrelation ◽  
House Prices ◽  
Conservation Easement ◽  
Agricultural Conservation ◽  
The Impact

scholarly journals How do house prices respond to mortgage supply?

2020 ◽  
Author(s):  
Guglielmo Barone ◽  
Francesco David ◽  
Guido de Blasio ◽  
Sauro Mocetti
Keyword(s):  
Real Estate ◽  
Spatial Heterogeneity ◽  
Causal Effect ◽  
House Prices ◽  
Data Driven ◽  
Supply Curve ◽  
Housing Supply ◽  
External Finance ◽  
The Impact

Abstract We examine the impact of household mortgages on house prices. Using biannual data on Italian cities in the period 2003–2015, we build an exogenous and fully data-driven indicator of mortgage supply stance and use it as instrument for actual extended mortgages. Our results indicate that mortgages have a positive and significant causal effect on house prices, with an estimated elasticity of around 0.1. The estimated effect is larger during the expansionary phase of the housing cycle. We also find evidence of significant spatial heterogeneity: mortgages push real estate values more in cities where the housing supply curve is less elastic or households are more dependent on external finance.

scholarly journals Consequences of ignoring variable and spatially-autocorrelated detection probability in spatial capture-recapture

2020 ◽  
Author(s):  
Ehsan M. Moqanaki ◽  
Cyril Milleret ◽  
Mahdieh Tourani ◽  
Pierre Dupont ◽  
Richard Bischof
Keyword(s):  
Spatial Heterogeneity ◽  
Spatial Autocorrelation ◽  
Detection Probability ◽  
Model Misspecification ◽  
Simulated Data ◽  
Parameter Estimates ◽  
Abundance Estimates ◽  
Credible Intervals ◽  
Capture Recapture ◽  
The Impact

AbstractContextSpatial capture-recapture (SCR) models are increasingly popular for analyzing wildlife monitoring data. SCR can account for spatial heterogeneity in detection that arises from individual space use (detection kernel), variation in the sampling process, and the distribution of individuals (density). However, unexplained and unmodeled spatial heterogeneity in detectability may remain due to cryptic factors, intrinsic and extrinsic to the study system.ObjectivesWe identify how the magnitude and configuration of unmodeled, spatially variable detection probability influence SCR parameter estimates.MethodsWe simulated realistic SCR data with spatially variable and autocorrelated detection probability. We then fitted a single-session SCR model ignoring this variation to the simulated data and assessed the impact of model misspecification on inferences.ResultsHighly autocorrelated spatial heterogeneity in detection probability (Moran’s I = 0.85 - 0.96), modulated by the magnitude of that variation, can lead to pronounced negative bias (up to 75%), reduction in precision (249%), and decreasing coverage probability of the 95% credible intervals associated with abundance estimates to 0. Conversely, at low levels of spatial autocorrelation (median Moran’s I = 0), even severe unmodeled heterogeneity in detection probability did not lead to pronounced bias and only caused slight reductions in precision and coverage of abundance estimates.ConclusionsUnknown and unmodeled variation in detection probability is liable to be the norm, rather than the exception, in SCR studies. We encourage practitioners to consider the impact that spatial autocorrelation in detectability has on their inferences and urge the development of SCR methods that can take structured unknown or partially unknown spatial variability in detection probability into account.

scholarly journals Consequences of ignoring variable and spatially autocorrelated detection probability in spatial capture-recapture

2021 ◽  
Author(s):  
Ehsan M. Moqanaki ◽  
Cyril Milleret ◽  
Mahdieh Tourani ◽  
Pierre Dupont ◽  
Richard Bischof
Keyword(s):  
Spatial Heterogeneity ◽  
Spatial Autocorrelation ◽  
Detection Probability ◽  
Model Misspecification ◽  
Simulated Data ◽  
Parameter Estimates ◽  
Reference Scenario ◽  
Abundance Estimates ◽  
Capture Recapture ◽  
The Impact

Abstract Context Spatial capture-recapture (SCR) models are increasingly popular for analyzing wildlife monitoring data. SCR can account for spatial heterogeneity in detection that arises from individual space use (detection kernel), variation in the sampling process, and the distribution of individuals (density). However, unexplained and unmodeled spatial heterogeneity in detectability may remain due to cryptic factors, both intrinsic and extrinsic to the study system. This is the case, for example, when covariates coding for variable effort and detection probability in general are incomplete or entirely lacking. Objectives We identify how the magnitude and configuration of unmodeled, spatially variable detection probability influence SCR parameter estimates. Methods We simulated SCR data with spatially variable and autocorrelated detection probability. We then fitted an SCR model ignoring this variation to the simulated data and assessed the impact of model misspecification on inferences. Results Highly-autocorrelated spatial heterogeneity in detection probability (Moran’s I = 0.85–0.96), modulated by the magnitude of the unmodeled heterogeneity, can lead to pronounced negative bias (up to 65%, or about 44-fold decrease compared to the reference scenario), reduction in precision (249% or 2.5-fold) and coverage probability of the 95% credible intervals associated with abundance estimates to 0. Conversely, at low levels of spatial autocorrelation (median Moran’s I = 0), even severe unmodeled heterogeneity in detection probability did not lead to pronounced bias and only caused slight reductions in precision and coverage of abundance estimates. Conclusions Unknown and unmodeled variation in detection probability is liable to be the norm, rather than the exception, in SCR studies. We encourage practitioners to consider the impact that spatial autocorrelation in detectability has on their inferences and urge the development of SCR methods that can take structured, unknown or partially unknown spatial variability in detection probability into account.

Dampak Insentif Pajak Perumahan Terhadap Potensi Penerimaan Pajak Negara Dan Peningkatan Pendapatan Masyarakat : Kasus Provinsi Papua

2016 ◽  
Vol 1 (1) ◽  
pp. 13-22
Author(s):  
Towaf Totok Irawan
Keyword(s):  
Low Income ◽  
House Prices ◽  
Tax Incentives ◽  
High Price ◽  
Short Term ◽  
Primary Input ◽  
State Revenue ◽  
Long Run ◽  
The Government ◽  
The Impact

Until now the government and private sector have not been able to address the backlog of 13.5 million housing units for ownership status and 7.6 million units for residential status. The high price of land has led to the high price of the house so that low-income communities (MBR) is not able to reach out to make a home purchase. In addition to the high price of land, tax factors also contribute to the high price of the house. The government plans to issue a policy for the provision of tax incentives, ie abolish VAT on home-forming material transaction. This policy is expected to house prices become cheaper, so the demand for housing increases, and encourage the relevant sectors to intensify its role in the construction of houses. It is expected to replace the lost tax potential and increase incomes. Analysis of the impact of tax incentives housing to potential state revenue and an increase in people's income, especially in Papua province is using the table IO because in addition to looking at the role each sector can also see the impact on taxes (income tax 21 Pph 25 Pph, VAT), and incomes (wage). Although in the short-term impact is still small, but very rewarding in the long run. Keywords: Backlog, Gross Input, Primary Input, Intermediate Input

Has tourism driven house prices in Germany? Time-varying evidence since 1870

2021 ◽  
pp. 135481662110088
Author(s):  
Sefa Awaworyi Churchill ◽  
John Inekwe ◽  
Kris Ivanovski
Keyword(s):  
World War Ii ◽  
Historical Data ◽  
House Prices ◽  
Housing Prices ◽  
Time Varying ◽  
Data Set ◽  
Time Series Modelling ◽  
The Impact ◽  
Tourism Flows ◽  
Non Parametric

Using a historical data set and recent advances in non-parametric time series modelling, we investigate the nexus between tourism flows and house prices in Germany over nearly 150 years. We use time-varying non-parametric techniques given that historical data tend to exhibit abrupt changes and other forms of non-linearities. Our findings show evidence of a time-varying effect of tourism flows on house prices, although with mixed effects. The pre-World War II time-varying estimates of tourism show both positive and negative effects on house prices. While changes in tourism flows contribute to increasing housing prices over the post-1950 period, this is short-lived, and the effect declines until the mid-1990s. However, we find a positive and significant relationship after 2000, where the impact of tourism on house prices becomes more pronounced in recent years.

scholarly journals The Impact of House Prices on Consumption in the UK: a New Perspective

Economica ◽  
2017 ◽  
Vol 85 (337) ◽  
pp. 92-123 ◽  
Author(s):  
Vivien Burrows
Keyword(s):  
House Prices ◽  
New Perspective ◽  
The Uk ◽  
The Impact

Understanding Affordability

2020 ◽  
Author(s):  
Geoffrey Meen ◽  
Christine Whitehead
Keyword(s):  
Low Income ◽  
Structural Changes ◽  
Home Ownership ◽  
House Prices ◽  
Household Formation ◽  
Policy Makers ◽  
Political Forces ◽  
The Uk ◽  
Developed World ◽  
The Impact

Affordability is, perhaps, the greatest housing problem facing households today, both in the UK and internationally. Even though most households are now well housed, hardship is disproportionately concentrated among low-income and younger households. Our failure to deal with their problems is what makes housing so frustrating. But, to improve outcomes, we have to understand the complex economic and political forces which underlie their continued prevalence. There are no costless solutions, but there are new policy directions that can be explored in addition to those that have dominated in recent years. The first, analytic, part of the book considers the factors that determine house prices and rents, household formation and tenure, housing construction and the roles played by housing finance and taxation. The second part turns to examine the impact of past policy and the possibilities for improvement - discussing supply and the impact of planning regulation, supply subsidies, subsidies to low-income tenants and attempts to increase home ownership. Rather than advocating a particular set of policies, the aim is to consider the balance of policies; the constraints under which housing policy operates; what can realistically be achieved; the structural changes that would need to occur; and the significant sacrifices that would have to be made by some groups if there are to be improvements for others. Our emphasis is on the UK but throughout the book we also draw on international experience and our conclusions have relevance to analysts and policy makers across the developed world.

scholarly journals Examining the spatial relationship between environmental health factors and house prices

2018 ◽  
Vol 11 (3) ◽  
pp. 353-398 ◽  
Author(s):  
Michael J. McCord ◽  
Sean MacIntyre ◽  
Paul Bidanset ◽  
Daniel Lo ◽  
Peadar Davis
Keyword(s):  
Air Quality ◽  
Environmental Quality ◽  
House Prices ◽  
Management Strategies ◽  
Noise Pollution ◽  
Well Being ◽  
Spatial Modelling ◽  
Content Type ◽  
The Impact ◽  
Health And Well Being

Purpose Air quality, noise and proximity to urban infrastructure can arguably have an important impact on the quality of life. Environmental quality (the price of good health) has become a central tenet for consumer choice in urban locales when deciding on a residential neighbourhood. Unlike the market for most tangible goods, the market for environmental quality does not yield an observable per unit price effect. As no explicit price exists for a unit of environmental quality, this paper aims to use the housing market to derive its implicit price and test whether these constituent elements of health and well-being are indeed capitalised into property prices and thus implicitly priced in the market place. Design/methodology/approach A considerable number of studies have used hedonic pricing models by incorporating spatial effects to assess the impact of air quality, noise and proximity to noise pollutants on property market pricing. This study presents a spatial analysis of air quality and noise pollution and their association with house prices, using 2,501 sale transactions for the period 2013. To assess the impact of the pollutants, three different spatial modelling approaches are used, namely, ordinary least squares using spatial dummies, a geographically weighted regression (GWR) and a spatial lag model (SLM). Findings The findings suggest that air quality pollutants have an adverse impact on house prices, which fluctuate across the urban area. The analysis suggests that the noise level does matter, although this varies significantly over the urban setting and varies by source. Originality/value Air quality and environmental noise pollution are important concerns for health and well-being. Noise impact seems to depend not only on the noise intensity to which dwellings are exposed but also on the nature of the noise source. This may suggest the presence of other externalities that arouse social aversion. This research presents an original study utilising advanced spatial modelling approaches. The research has value in further understanding the market impact of environmental factors and in providing findings to support local air zone management strategies, noise abatement and management strategies and is of value to the wider urban planning and public health disciplines.

Sign in / Sign up

Export Citation Format

Share Document