The Implicit Value of Irrigation Through Parcel Level Hedonic Price Modeling

This paper relies on data associated 2,100 agricultural land sale transactions across two major Nebraska Watersheds (the Republican and Central Platte) over the 2000 to 2008 time period. The sales were spatially referenced (digitized into a GIS) in order to quantify and geo-spatially predict and map the implicit values of irrigation through the use of hedonic price modeling. Marginal implicit prices vary substantially across subwatersheds (natural resource districts), and the contribution of irrigation to sale prices is directly related to the extent to dependency of production agriculture on irrigation. This information is now currently being used to evaluate the economic efficiency of recent irrigation retirement programs and to help ensure that current and future retirement programs are cost-effective through targeting that retires irrigation land with the greatest hydrologic impact on water resources for the lowest cost.Farm Management, Production Economics, Resource /Energy Economics and Policy,

HEDONIC PRICING OF CLIMATE CHANGE IMPACTS TO HOUSEHOLDS IN GREAT BRITAIN

This study investigates the amenity value of climate to British households. By using the hedonic price approach, the marginal willingness to pay for small changes in climate variables, specified as averages and ranges, is derived. The estimates suggest that British people would typically prefer a greater distribution of precipitation across the seasons (i.e. holding annual precipitation constant, drier summers and wetter winters are preferred). Higher temperature ranges are likely to reduce welfare. Moderate global warming with warmer winters and drier summers might thus benefit British households. In particular we find that those places with little or average range in rainfall like Nottingham and those with a huge range of annual temperature like the Boroughs of London might profit. Places already characterized by a broad range of annual precipitation like Aberdare in Mid Glamorgan on the other hand would most likely lose from climate change.amenity values, climate change, environmental valuation, Great Britain, hedonic pricing

The impact of location on housing prices: applying the Artificial Neural Network Model as an analytical tool.

The location of a residential property in a city directly affects its market price. Each location represents different values in variables such as accessibility, neighbourhood, traffic, socio-economic level and proximity to green areas, among others. In addition, that location has an influence on the choice and on the offer price of each residential property. The development of artificial intelligence, allows us to use alternative tools to the traditional methods of econometric modelling. This has led us to conduct a study of the residential property market in the city of Valencia (Spain). In this study, we will attempt to explain the aspects that determine the demand for housing and the behaviour of prices in the urban space. We used an artificial neutral network as a price forecasting tool, since this system shows a considerable improvement in the accuracy of ratings over traditional models. With the help of this system, we attempted to quantify the impact on residential property prices of issues such as accessibility, level of service standards of public utilities, quality of urban planning, environmental surroundings and other locational aspects.

BIG DATA FOR COMPREHENSIVE ANALYSIS OF REAL ESTATE MARKET

This Culminating Experience Project explored the application of big data in the real estate industry in order to address the problem of analyzing the accurate property estimates value. The research questions were: (Q1): What are the benefits and advantages of utilizing big data in the real estate market? (Q2): What are the trends in the application of big data in the real estate market? (Q3): What are the challenges in applying big data in the real estate market? (Q4): What are the methods and processes of applying big data in appraisal of assets in the real estate market? To answer these four questions, this study used qualitative and quantitative methodology, content analysis conducted on data collected through Google Scholar, and One Search for industry reports, conference papers, and select literature about big data adoption trends in the real estate industry. The findings were as follows: (Q1): The benefits of using big data analytics are to help clients to make the right decisions and advice, have higher efficiency for appraisals, better risk evaluation of risk in the real estate industry simplifying applications in valuations and pricing. (Q2): there is anecdotal evidence that real estate has already started adopting big data. Adoption is most likely to be beneficial for first mover industry players at the top of the industry pyramid including investment banks, commercial banks, and mortgage banks that hold the highest interest in the real estate industry. (Q3): complexity of big data solutions and the costs of implementation are a major challenge while smaller players such as real estate agents and brokers do not find utility or justification for the huge investment in big data. (Q4): the development of algorithms remains as the main process of applying big data solutions as there are no off-the-shelf big data solutions for the real estate industry. Adoption of Machine learning (ML) and Artificial Intelligence (AI) in real estate would help buyers and sellers to learn from data and make informed decisions. The conclusions of the culminating experience project are Real Estate Industry has a low adoption of big data solutions because many of the players in the industry have not yet learned how to translate big data to business objectives. Areas of further studies include the development of models and algorithms for use by the real estate industry

Understanding Combined Sewer Overflow (CSO) and Green Infrastructure Interaction in New Jersey : An Economic Analysis

New Jersey, as a coastal area, has historically struggled with a variety of problems stemming from stormwater runoff, which have only grown more prevalent and harmful as urbanization and climate change have taken their toll. One such issue that has emerged in recent years is the prevalence of combined sewer overflows (CSOs). These sewer systems are common in urban areas in the United States and abroad, and increased urbanization has them not only obsolete but a persistent danger, as their discharges can contaminate waterways and affect human health. While municipalities across the United States are beginning to move towards mitigating or replacing CSO systems, many areas still struggle to do so due to cost. Further, few studies have been done to understand the full cost of CSOs, as externalities such as effects on society or housing markets are largely understudied. As such, this study proposes a number of interlinked economic valuations to understand the costs of CSOs and the benefits of their solutions. To understand costs, we utilize a hedonic analysis using observable real estate data to understand the economic impact of CSOs on the housing market. As CSOs are heavily regulated by the EPA, there is significant value in also understanding the benefit of possible solutions to the problems that CSOs represent. To this end, we analyze green infrastructure, which has been used extensively around the United States and abroad to cheaply and effectively limit CSO discharges. We use a choice-experiment survey to delineate willingness to pay in target cities, and to understand preferences of residents in terms of green infrastructure capabilities and payment vectors for funding such projects. Finally, we use an ArcGIS linked framework to analyze the potential benefit of green infrastructure in terms of runoff reductions, and understand what land use types are ideal for installation. The combination of these economic analyses should give a more complete picture of the full cost of these fixtures than has existed in the literature to date, and can be useful to researchers and decision makers alike

Modeling water resources management at the basin level: review and future directions

Water quality / Water resources development / Agricultural production / River basin development / Mathematical models / Simulation models / Water allocation / Policy / Economic aspects / Hydrology / Reservoir operation / Groundwater management / Drainage / Conjunctive use / Surface water / GIS / Decision support systems / Optimization methods / Water supply

Evaluating remote economic benefits of watershed-scale acid mine drainage restoration

In 2011, the West Virginia Abandoned Mine Lands and Reclamation Program (WVAML) implemented a watershed-scale restoration project in Three Fork Creek, a 103 square-mile watershed in rural West Virginia impaired by acid mine drainage (AMD). This was accomplished through the installation of four in-stream lime dosers in 2011. Due to the great capital investment in the restoration of Three Fork Creek and an interest in applying this watershed-scale approach to other watersheds throughout West Virginia, it is of great interest to demonstrate the successes of this remediation. It has already been demonstrated that the watershed has visibly improved and is rebounding ecologically. Water quality has improved dramatically and dissolved aluminum concentrations have decreased by almost 98% (USEPA, 2016). However, it is difficult to quantify the economic benefit of watershed restoration due to the remote and non-market values associated with it. This is especially difficult and understudied in small rural watersheds that lack an established recreation-based industry. This study focused on property value as a remote benefit of watershed restoration to address the gap in current research. We conducted a spatial analysis of assessed property values in Three Fork Creek after ten years of watershed-scale AMD treatment to find changes in streamside property values over time due to environmental improvements. On average, property values increased 85% throughout the watershed during the study period. Property value changes increased positively as areas were focused closer to the stream. The change in property values was greatest within the 0.25-mile buffer at 181%. The results demonstrate the ability of watershed-scale remediation to produce local economic benefits, including remote benefits in Appalachia. This study furthers the growing research in quantifying successes of AMD remediation, as watershed-wide treatment of AMD is unique to the past few decades. Further research that could be accomplished utilizing the results from this study includes quantifying the total economic benefits post-AMD remediation, developing more accurate models to predict the total economic benefits expected from AMD remediation in small rural watersheds, and further analyzing the relationship between property value and water quality