This dissertation introduces advanced statistical and econometric methods in two distinct areas of non-market valuation: benefit transfer (BT) and hedonic analysis. While the first and the third chapters address the challenge of estimating the societal benefits of prospective environmental policy changes by adopting locally weighted regression (LWR) technique in an environmental valuation context, the second chapter combines the output from traditional hedonic regression and matching estimators and provides guidance on the choice of model with low risk of bias in housing market studies. The economic and societal benefits associated with various environmental conservation programs, such as improvement in water quality, or increment in wetland acreages, can be directly estimated using primary studies. However, conducting primary studies can be highly resource-intensive and time-consuming as they typically involve extensive data collection, sophisticated models, and a considerable investment of financial and human resources. As a result, BT offers a practical alternative, which involves employing valuation estimates, functions, or models from prior primary studies to predict the societal benefit of conservation policies at a policy site. Existing studies typically fit one single regression model to all observations within the given metadata and generate a single set of coefficients to predict welfare (willingness-to-pay) in a prospective policy site. However, a single set of coefficients may not reflect the true relationship between dependent and independent variables, especially when multiple source studies/locations are involved in the data-generating process which, in turn, degrades the predictive accuracy of the given meta-regression model (MRM). To address this shortcoming, we employ the LWR technique in an environmental valuation context. LWR allows an estimation of a different set of coefficients for each location to be used for BT prediction. However, the empirical exercise carried out in the existing literature is rigorous from a computational perspective and is cumbersome for practical adaptation. In the first chapter, we simplify the experimental setup required for LWR-BT analysis by taking a closer look at the choice of weight variables for different window sizes and weight function settings. We propose a pragmatic solution by suggesting "universal weights" instead of striving to identify the best of thousands of different weight variable settings. We use the water quality metadata employed in the published literature and show that our universal weights generate more efficient and equally plausible BT estimates for policy sites than the best weight variable settings that emerge from a time-consuming cross-validation search over the entire universe of individual variable combinations. The third chapter expands the scope of LWR to wetland meta-data. We use a conceptually similar set of weight variables as in the first chapter and replicate the methodological approach of that chapter. We show that LWR, under our proposed weight settings, generates substantial gain in both predictive accuracy and efficiency compared to the one generated by standard globally-linear MRM. Our second chapter delves into a separate yet interrelated realm of non-market valuation, i.e., hedonic analysis. Here, we explore the combined inferential power of traditional hedonic regression and matching estimators to provide guidance on model choice for housing market studies where researchers aim to estimate an unbiased binary treatment effect in the presence of unobserved spatial and temporal effects. We examine the potential sources of bias within both hedonic regression and basic matching. We discuss the theoretical routes to mitigate these biases and assess their feasibility in practical contexts. We propose a novel route towards unbiasedness, i.e., the "cancellation effect" and illustrate its empirical feasibility while estimating the impact of flood hazards on housing prices. / Doctor of Philosophy / This dissertation introduces novel statistical and econometric methods to better understand the value of environmental resources that do not have an explicit market price, such as the benefits we get from the changes in water quality, size of wetlands, or the impact of flood risk zoning in the sales price of residential properties. The first and third chapters tackle the challenge of estimating the value of environmental changes, such as cleaner water or more wetlands. To figure out how much people benefit from these changes, we can look at how much they would be willing to pay for such improved water quality or increased wetland area. This typically requires conducting a primary survey, which is expensive and time-consuming. Instead, researchers can draw insights from prior studies to predict welfare in a new policy site. This approach is analogous to applying a methodology and/or findings from one research work to another. However, the direct application of findings from one context to another assumes uniformity across the different studies which is unlikely, especially when past studies are associated with different spatial locations. To address this, we propose a ``locally-weighting" technique. This places greater emphasis on the studies that closely align with the characteristics of the new (policy) context. Determining the weight variables/factors that dictate this alignment is a question that requires an empirical investigation. One recent study attempts this locally-weighting technique to estimate the benefits of improved water quality and suggests experimenting with different factors to find the similarity between the past and new studies. However, their approach is computationally intensive, making it impractical for adaptation. In our first chapter, we propose a more pragmatic solution---using a "universal weight" that does not require assessing multiple factors. With our proposed weights in an otherwise similar context, we find more efficient and equally plausible estimates of the benefits as previous studies. We expand the scope of the local weighting to the valuation of gains or losses in wetland areas in the third chapter. We use a conceptually similar set of weight variables and replicate the empirical exercise from the first chapter. We show that the local-weighting technique, under our proposed settings, substantially improves the accuracy and efficiency of estimated benefits associated with the change in wetland acreage. This highlights the diverse potential of the local weighting technique in an environmental valuation context. The second chapter of this dissertation attempts to understand the impact of flood risk on housing prices. We can use "hedonic regression" to understand how different features of a house, like its size, location, sales year, amenities, and flood zone location affect its price. However, if we do not correctly specify this function, then the estimates will be misleading. Alternatively, we can use "matching" technique where we pair the houses inside and outside of the flood zone in all observable characteristics, and differentiate their price to estimate the flood zone impact. However, finding identical houses in all aspects of household and neighborhood characteristics is practically impossible. We propose that any leftover differences in features of the matched houses can be balanced out by considering where the houses are located (school zone, for example) and when they were sold. We refer to this route as the "cancellation effect" and show that this can indeed be achieved in practice especially when we pair a single house in a flood zone with many houses outside that zone. This not only allows us to accurately estimate the effect of flood zones on housing prices but also reduces the uncertainty around our findings.

The purpose of this dissertation is to examine the extent of competition between generic products and therapeutic substitutes under different regulatory regimes in the European Union (EU) pharmaceutical industry. In particular, this study investigates generic competition among the five largest European pharmaceutical markets; the United Kingdom, Germany, France, Italy and Spain, with comprehensive IMS data for 10 years (1994-2003), in order to estimate the effect of generic entry on drug prices at the product level. This analysis finds that generic entry has a negative effect on prices in countries with free pricing originator market, whereas in EU countries with strict price and reimbursement regulation, generic competition is ineffective and/or counterproductive. Fewer generics and less competitive late entrants are consistent with incentives in regulated environments: low regulated prices for originator products discourage generic entry following patent expiration. These findings suggest that regulation of both manufacturers' prices and retail pharmacy prices undermines price competition in the off-patent sector, and that budgetary savings from generic price competition are not realized in countries with strict regulatory systems.

During the last two decades, the European Union (EU) has experienced closer market integration through the removal of trade barriers, the establishment of a single market, and the reduction of exchange rate volatility. In addition, there have been several structural reforms in product markets designed to increase competition, monitor cross-country price differences and increase transparency. One anticipated effect of market integration is price convergence, because of the reduced potential for price discrimination across the EU. This dissertation explores market integration and price convergence in the European pharmaceutical market, which is the fifth largest industry in the EU. Since 1985, many EU directives have been adopted to achieve a single EU-wide pharmaceutical market, with the aim of enhancing the quality of life for European citizens and the European pharmaceutical industry's competitiveness and research and development capability. Using annual 1994--2003 data from five EU countries on prices of drugs used to treat cardiovascular disease, this dissertation explains how the integration process has affected cross-country drug price dispersion in the EU. The results show strong evidence of price convergence in the pharmaceutical market, with long term price differences arising from country fixed effects.

The buildings sector consumes approximately 40% of energy in the United States, and presents a major opportunity for reducing society's energy consumption and environmental impact. Given the potential downside impacts of climate change and resource depletion, it is imperative that the construction industry deliver buildings that meet owner requirements while using less energy and natural resources. In response to this challenge, the construction industry has adopted voluntary green building programs that provide guidelines for construction projects wishing to reduce their environmental impact. Green building programs also present the opportunity for those pushing beyond the status quo to receive increased recognition and market visibility; however, certification under these programs is not without an added cost. The added cost of certification varies by project, but building owners and builders must be able to justify this added cost through increased market recognition and sales and leasing prices. Given the relatively low recognition of a price premium for green certified residential properties by the real estate appraisal community and financial institutions, a need exists to demonstrate the added market recognition of these homes. Through the development of a hedonic regression pricing model this study isolates the effects of green building certification on housing sales prices, in order to prove the hypothesis that a significant increase in sales price is associated with green certified housing.

The nation’s economy depends heavily on mobility of goods and people. As communities look to improve mobility, many options can be considered, including roadway improvements, congestion-pricing options such as dynamic tolling and toll lanes, and public transit. Investment in public transit services can come in the form of increased and enhanced bus services, including bus rapid transit (BRT), as well as rail transit investments. As BRT continues to grow in popularity in the United States, a better understanding of the mode’s impacts on land uses and economic development is needed. One method of assessing the mode’s impacts is by examining the market value of properties with access to BRT stations. Based on land-rent theory, it is hypothesized that people will be willing to pay a premium for convenient and reliable access via BRT to the central business district (CBD) or other locations with employment, educational, recreational, and shopping opportunities. Very little research has been conducted on BRT as it operates in the present day in the United States. For this work, the hypothesis is that the BRT stations have a positive impact on the market value of residential properties. To test this hypothesis, hedonic price regression models are used to estimate the impact of access to BRT stations on the sale prices of surrounding single-family homes using a case study of the HealthLine BRT system in Cleveland, Ohio that began operating in 2008. Three time periods were examined: 2004, the year construction began; 2008–2009, after the HealthLine BRT service began operation; and 2010–2011, the latest year for which sales data are available. Despite a documented decline in median sale prices of single-family homes in the city of Cleveland from 2005 to 2011, overall results of the analysis were mixed. Although it was prior to the opening of the BRT system, the 2004 data did not show any impacts of the stations on surrounding home sale prices. For the 2008–2009 data, positive and statistically significant impacts were found; however, the positive impacts did not persist in the 2010–2011 data. It would likely be necessary to seek out additional years of data to fully answer the question posed by this research. It is important for decision-makers to have the most accurate and most recent information on the benefits and costs of all transportation alternatives, including BRT. The research presented herein makes a significant contribution to filling the current gap in quantitative research on the subject and provides planners, policymakers, and the transit industry with the best information possible to make sound transit investment decisions in their communities.

This paper analyzes how the COVID-19 pandemic has affected the Swedish housing market, and in particular prices and shifts in trends. Different classes of housing objects in various counties are investigated. Combining web scraped housing data for the entirety of Sweden between 2016-01-01 and 2021-03-31, including economic, demographic, socioeconomic and locational data, a hedonic regression model is used to estimate how different variables influence the housing price. The base model is subsequently used to investigate if statistically significant structural breaks exist in relation to the COVID-19 pandemic for the different object types in the entire Swedish market and in certain specific counties. Structural breaks are found for the housing object types ‘Fritidshus’, ‘Lägenhet’ and ‘Radhus’ in the entire Swedish market and for “Villa” in Stockholm county shortly after the pandemic outbreak, suggesting there is evidence for a pandemic infused shift in housing price regime on the Swedish housing market for these object types in stated areas. Splitting the hedonic regression model into three, one pooled regression, one before and one after the identified breaks, and comparing the shifts in impact of the housing price determinants suggests different pandemic effects on different object types. The result indicates that for the object types ‘Lägenhet’ in the entire country and for ‘Villa’ in Stockholm county, living area has an increased impact on the price while the locational variable population density has a decreased impact after the breakpoint date compared to before. This could suggest that for permanent housing objects in these regions, living area might have become increasingly valued over location during the pandemic. The results further indicate the direct opposite effect on the shifted impact in living area and the population density for the price of the temporary housing type Fritidshus in entire Sweden. However, an indication for increased impact of the areas socioeconomic level is noted for all these three object types. These results hold as a ground for further research in the subject.

Due to the serious impact wind damage has on homes in the Gulf Coast region, policy makers, community developers, and homeowners are seeking ways to lessen impacts. One potential tool to increase properties’ resiliency in the event of a periodic and catastrophic event is wind mitigation, the process of adding features to a building, i.e. a house, to increase the strength of the structure amid a storm such as a hurricane. In this research, I evaluate the tiers of FORTIFIED homes as the mitigation strategies. I use Zillow ZTRAX and Institute of Business and Home Safety data to estimate how each level of FORTIFIED home designation affects property value. The results show FORTIFIED Gold designation on a new home has a 0%-8.4% increase on property value. I place my finding into a BCA of FORTIFIED designation to evaluate how this one benefit fits into the greater picture.

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Abstract This thesis is a descriptive empirical research about the connection between architectonic quality and housing prices. The architectonic quality is defined in this research through the institutional concept of quality as it is understood inside the profession of architecture, which defines what is regarded as good architecture and which factors connected to designers can generate architectonic quality. By analyzing debt-free market prices of apartments, it is studied how well the institutional concept of quality corresponds to consumers’ appreciation. The research uses hedonic price models as a research method, which is a commonly used method for analyzing housing prices in the field of economics. The empirical data of the research consists of the apartment buildings located in the southern downtown area of Helsinki and the real estate transactions in them during the years 1980–2008. The factors of the architectonic quality in this data are collected and developed especially for this research. The main research question is: Is there a connection between the factors of architectonic quality and housing prices? More precise sub-questions are: Are education, experience, or merits of the building designer relevant factors of housing prices? Are the architectonic style or architectonic appreciation of the building relevant factors of housing prices? In addition, it is studied if there is a correlation between the architectonic view and housing prices, which would indicate that the architectonic quality causes also economic externalities. The result of the research is that the studied factors of the architectonic quality and housing prices are connected. The education, experience and merits of the building designer are all factors which are perceived to increase apartment prices. It was also revealed that the architectonic style and the architectonic appreciation of the building correlate with the housing prices. Moreover, a clear price premium related to architectonic view can be found in the analysis. However, all the factors employed do not give unambiguous results, which gives reason to study the differences of used factors and raises further research questions. / Tiivistelmä Tämä väitöstutkimus on kuvaileva empiirinen tutkimus arkkitehtonisen laadun yhteydestä asuntojen hintoihin. Arkkitehtoninen laatu määrittyy tutkimuksessa institutionaalisen laatukäsityksen kautta arkkitehtuurin alan sisäisenä näkemyksenä siitä, mitä pidetään hyvänä arkkitehtuurina ja mitkä rakennusten suunnittelijoihin liitetyt tekijät voivat edesauttaa arkkitehtonisen laadun syntymistä. Analysoimalla asuntojen velattomia myyntihintoja selvitetään, miten hyvin tutkimuksessa määritelty arkkitehtuurin institutionaalinen laatukäsitys vastaa kuluttajien arvostusta. Analyysimenetelmänä käytetään hedonista regressiomallinnusta, joka on taloustieteissä yleinen asuntojen hintojen tutkimisessa käytetty menetelmä. Empiirinen aineisto tutkimuksessa koostuu Helsingin eteläisen kantakaupungin alueen rakennuksista sekä niissä käydyistä asuntokaupoista vuosina 1980–2008. Aineiston arkkitehtonisen laadun muuttujat ovat tätä tutkimusta varten kehitettyjä ja kerättyjä. Päätutkimuskysymys on, onko arkkitehtonista laatua mittaavilla muuttujilla yhteys asuntojen hintoihin. Tarkentavia apukysymyksiä ovat seuraavat: Onko rakennussuunnittelijan koulutuksella, kokemuksella ja ansioituneisuudella merkitystä asuntojen hintatekijöinä? Onko rakennuksen arkkitehtonisella tyylillä tai arkkitehtonisella arvostuksella merkitystä asuntojen hintatekijöinä. Lisäksi tutkitaan, onko arkkitehtoniselle näkymälle löydettävissä asuntojen hinnoissa ilmenevä korrelaatio, joka indikoisi sitä, että arkkitehtonisella laadulla on myös taloudellisia ulkoisvaikutuksia. Tutkimuksen tulokseksi saadaan, että tutkituilla arkkitehtonisen laadun muuttujilla on havaittava yhteys asuntojen hintoihin. Sekä rakennussuunnittelijan koulutukseen, kokemukseen että ansioituneisuuteen liitettyjen muuttujien havaitaan tutkimuksessa olevan asuntojen hintaa nostavia tekijöitä. Myös rakennuksen arkkitehtoninen tyyli ja arkkitehtoninen arvostus osoittavat selkeän yhteyden asuntojen hintoihin. Niin ikään arkkitehtoniselle näkymälle löydetään suoritetuissa analyyseissä selkeä asuntojen hinnoissa havaittava preemio eli hintaero. Kaikki tutkitut mittarit eivät kuitenkaan anna täysin yksiselitteisiä tuloksia, mikä antaa aihetta tarkastella käytettyjen mittareiden eroavaisuuksia ja synnyttää jatkotutkimuskysymyksiä.

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