Housing risk and return: Evidence from a housing asset-pricing model

This paper investigates the risk-return relationship in determination of housing asset pricing. In so doing, the paper evaluates behavioral hypotheses advanced by Case and Shiller (1988, 2002, 2009) in studies of boom and post-boom housing markets. The paper specifies and tests a multi-factor housing asset pricing model. In that model, we evaluate whether the market factor as well as other measures of risk, including idiosyncratic risk, momentum, and MSA size effects, have explanatory power for metropolitan-specific housing returns. Further, we test the robustness of the asset pricing results to inclusion of controls for socioeconomic variables commonly represented in the house price literature, including changes in employment, affordability, and foreclosure incidence. We find a sizable and statistically significant influence of the market factor on MSA house price returns. Moreover we show that market betas have varied substantially over time. Also, results are largely robust to the inclusion of other explanatory variables, including standard measures of risk and other housing market fundamentals. Additional tests of model validity using the Fama-MacBeth framework offer further strong support of a positive risk and return relationship in housing. Our findings are supportive of the application of a housing investment risk-return framework in explanation of variation in metro-area cross-section and time-series US house price returns. Further, results strongly corroborate Case-Shiller survey research indicating the importance of speculative forces in the determination of U.S. housing returns

Sentiment Analysis on New York Times Articles Data

Sentiment Analysis on New York Times Coverage Data Departmental Affiliation: Data Science/ Political Science College of Arts and Sciences The extant political science literature examines media coverage of immigration and assesses the effect of that coverage on partisanship in the United States. Immigration is believed to be a unique factor that induces large- scale changes in partisanship based on race and ethnicity. The negative tone of media coverage pushes non-Latino Whites into the Republican Party, while Latinos trend toward the Democratic Party. The aim for this project is to look at New York time data in order to identify how much immigration is covered in newspaper outlets, specifically Latino immigration, and to determine the overall tone of these stories. In this research, we seek to determine individual articles take a positive, neutral or negative stance. We achieve this using a dictionary-based approach, meaning we look at individual words to assess if it has a positive, neutral or negative connotation. We train our data using publicly accessible sentiment dictionaries such as VADER (Valence Aware Dictionary and Sentiment Reasoner). However, this task can be difficult because certain words can be dynamic and may pertain to a positive or negative sentiment in context of the article. In order to resolve this issue, we use reliability measures to ensure that the words of high frequencies are in the correct sphere of negative, neutral, and positive light. Information about the Author(s): Faculty Sponsor(s): Professor Gregg B. Johnson and Professor Karl Schmitt Student Contact: Gabriel Carvajal – [email protected]

Boundary rigidity for Lagrangian submanifolds, non-removable intersections, and Aubry-Mather theory

We consider Lagrangian submanifolds lying on a fiberwise strictly convex hypersurface in some cotangent bundle or, respectively, in the domain bounded by such a hypersurface. We establish a new boundary rigidity phenomenon, saying that certain Lagrangians on the hypersurface cannot be deformed (via Lagrangians having the same Liouville class) into the interior domain. Moreover, we study the "non-removable intersection set" between the Lagrangian and the hypersurface, and show that it contains a set with specific dynamical behavior, known as Aubry set in Aubry-Mather theory.Comment: The main new point of this revised and substantially enlarged version, with G.P. Paternain as new co-author, is the relation between non-removable intersections and Aubry-Mather theor

Housing Risk and Return: Evidence From a Housing Asset-Pricing Model

This paper investigates the risk-return relationship in determination of housing asset pricing. In so doing, the paper evaluates behavioral hypotheses advanced by Case and Shiller (1988, 2002, 2009) in studies of boom and post-boom housing markets. The paper specifies and tests a housing asset pricing model (H-CAPM), whereby expected returns of metropolitan-specific housing markets are equated to the market return, as represented by aggregate US house price time-series. We augment the model by examining the impact of additional risk factors including aggregate stock market returns, idiosyncratic risk, momentum, and Metropolitan Statistical Area (MSA) size effects. Further, we test the robustness of H-CAPM results to inclusion of controls for socioeconomic variables commonly represented in the house price literature, including changes in employment, affordability, and foreclosure incidence. Consistent with the traditional CAPM, we find a sizable and statistically significant influence of the market factor on MSA house price returns. Moreover we show that market betas have varied substantially over time. Also, we find the basic housing CAPM results are robust to the inclusion of other explanatory variables, including standard measures of risk and other housing market fundamentals. Additional tests of the validity of the model using the Fama-MacBeth framework offer further strong support of a positive risk and return relationship in housing. Our findings are supportive of the application of a housing investment risk-return framework in explanation of variation in metro-area cross-section and time-series US house price returns. Further, results strongly corroborate Case-Shiller behavioral research indicating the importance of speculative forces in the determination of U.S. housing returns.asset pricing, house price returns, risk factors

Zero-Rating, One Big Mess: Analyzing Differential Pricing Practices of European MNOs

Zero-rating, the practice of not billing data traffic that belongs to certain applications, has become popular within the mobile ecosystem around the globe. There is an ongoing debate whether mobile operators should be allowed to differentiate traffic or whether net neutrality regulations should prevent this. Despite the importance of this issue, we know little about the technical aspects of zero-rating offers since the implementation is kept secret by mobile operators and therefore is opaque to end-users and regulatory agencies. This work aims to independently audit classification practices used for zero-rating of four popular applications at seven different mobile operators in the EU. We execute and evaluate more than 300 controlled experiments within domestic and internationally roamed environments and identify potentially problematic behavior at almost all investigated operators. With this study, we hope to increase transparency around the current practices and inform future decisions and policies

Sentiment Analysis on New York Times Articles Data

Sentiment Analysis on New York Times Coverage Data Departmental Affiliation: Data Science/ Political Science College of Arts and Sciences The extant political science literature examines media coverage of immigration and assesses the effect of that coverage on partisanship in the United States. Immigration is believed to be a unique factor that induces large- scale changes in partisanship based on race and ethnicity. The negative tone of media coverage pushes non-Latino Whites into the Republican Party, while Latinos trend toward the Democratic Party. The aim for this project is to look at New York time data in order to identify how much immigration is covered in newspaper outlets, specifically Latino immigration, and to determine the overall tone of these stories. In this research, we seek to determine individual articles take a positive, neutral or negative stance. We achieve this using a dictionary-based approach, meaning we look at individual words to assess if it has a positive, neutral or negative connotation. We train our data using publicly accessible sentiment dictionaries such as VADER (Valence Aware Dictionary and Sentiment Reasoner). However, this task can be difficult because certain words can be dynamic and may pertain to a positive or negative sentiment in context of the article. In order to resolve this issue, we use reliability measures to ensure that the words of high frequencies are in the correct sphere of negative, neutral, and positive light. Information about the Author(s): Faculty Sponsor(s): Professor Gregg B. Johnson and Professor Karl Schmitt Student Contact: Gabriel Carvajal – [email protected]

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