The first chapter studies the consequences of joint ownership between real estate agencies and mortgage lenders for consumers, lenders, and mortgage market structure. I construct a novel data set which matches home buyers’ real estate agencies, lenders, and loan characteristics while tracking ownership of lenders and agencies over time. Using hand-collected data for over 100 mergers involving real estate agencies or lenders, I implement a staggered differences-in-differences strategy that compares lender-agency pairs which are jointly owned due to horizontal mergers between real estate agencies to lender-agency pairs that are never jointly owned. After merging, lenders double their loan shares within jointly owned real estate agencies with little impact on a lender’s CBSA market share. Buyers who use a lender jointly owned with their real estate agency pay interest rates 9 basis points higher, amounting to $225 in additional interest per year on the average loan. However, I find no evidence that home buyers’ credit characteristics, delinquency rates, or transaction speed change following these mergers. Finally, I develop a structural model of the mortgage market to study the welfare implications of mergers under counterfactual policies. I find that completely banning mergers harms consumers, while allowing mergers that promote competition can improve consumer welfare. The second chapter studies the time cost of congestion on public transit. Congestion on public transit is a poorly understood externality for those who choose to use it. In this chapter, I focus on one portion of this externality: time cost. I use subway ridership data from the Washington, DC metro area. I instrument for ridership using the 2018-2019 government shutdown and employing a two-stage least squares approach, I find that ridership has a strong, positive relationship with trip time, and that this effect is stronger at high levels of ridership. In addition, I reject that this effect is through longer wait times or slower train speeds at higher levels of ridership. Quantifying the value of this time cost and the mechanism can inform policy decisions relating to public transportation expansion and cost-benefit analysis.