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Scholarly Works (2 results)

  • Thesis
  • Peer Reviewed
UCLA Electronic Theses and Dissertations (2024)

In Chapter 1 of this dissertation, I document a negative impact of operational capacity pressures of mortgage lenders on the pass-through from mortgage-backed securities (MBS) yields to mortgage rates. In the aggregate time series, I find mortgage-rate pass-through decreases by 12.9% significantly when loan processing cycle increases by 3.3 days; In the panel data, I find mortgage rate pass-through decreases by 10% significantly when lender capacity utilization rate increases by one standard deviation. Moreover, using a difference-indifference regression with staggered treatment, I find mortgage spread keeps decreasing after the authorization of remote online notarization, which facilitates loan origination operations and relieves lender capacity pressures.

In Chapter 2, I continue to explore mortgage rate pass-through in the primary market by first documenting its properties and then using a search model to explain the mechanism. I find the average pass-through from MBS yields to mortgage rates is 85% (imperfectness); Moreover, the pass-through decreases from 93% to 64% when MBS yield shifts from one standard deviation above its median to one standard deviation below its median (rate dependency). By using a search model involving both the mortgage market and the labor market, I highlight the key role of labor market frictions in causing operational capacity pressures on mortgage lenders and higher markups in mortgage rates, which explains the observed imperfectness and rate dependency of mortgage rate pass-through, as well as the negative impact of lender capacity pressures on mortgage rate pass-through documented in Chapter 1.

In Chapter 3, I examine whether borrowers are more or less satisfied with nonbank mortgage servicers versus bank servicers, given the rising market share of nonbank servicers in the recent decade. The prevalence of nonbank servicers brings benefits such as increasing market competition, promoting technological advances, and lowering capacity pressures in the mortgage market; but also raises supervisory concerns as the regulatory frameworks for nonbank mortgage players are less mature than for traditional banks. Using complaint data filed with the Consumer Financial Protection Bureau (CFPB), I find higher complaint ratios for nonbanks. To provide evidence that this is not driven by unobservable consumer differences, I use the regional variation in bank capital ratios as an instrument for nonbank penetration levels. Regions with higher predicted market shares of nonbanks also have higher complaint ratios, verifying consumers are less satisfied with the services provided by nonbanks.

    Cover page: Essays on the Mortgage Market
    • Article
    • Peer Reviewed
    UC Irvine Previously Published Works (2023)
    BACKGROUND: Early identification of severe acute pancreatitis (SAP) is key to reducing mortality and improving prognosis. We aimed to establish a radiomics model and nomogram for early prediction of acute pancreatitis (AP) severity based on contrast-enhanced computed tomography (CT) images. METHODS: We retrospectively analyzed 215 patients with first-episode AP, including 141 in the training cohort (87 men and 54 women, mean age 51.37±16.09 years) and 74 in the test cohort (40 men and 34 women, mean age 55.49±17.83 years). Radiomics features were extracted from portal venous phase images based on pancreatic and peripancreatic regions. The light gradient boosting machine (LightGBM) algorithm was used for feature selection, a logistic regression (LR) model was established and trained by 10-fold cross-validation, and a nomogram was established based on the best features. The models predictive performance was evaluated according to the area under the curve (AUC) of the receiver operating characteristic (ROC) curve, sensitivity, specificity, and accuracy. RESULTS: A total of 13 optimal radiomics features were selected by LightGBM for LR model building. The AUC of the radiomics (LR) model was 0.992 [95% confidence interval (CI): 0.963-0.996] in the training cohort, 0.965 (95% CI: 0.924-0.981) in the validation cohort, and 0.894 (95% CI: 0.789-0.966) in the test cohort. The sensitivity was 0.862 (95% CI: 0.674-0.954), the specificity was 0.800 (95% CI: 0.649-0.899), and the accuracy was 0.824 (95% CI: 0.720-0.919). The nomogram based on the 13 radiomics features showed that SAP would be predicted when the total score was greater than 124. CONCLUSIONS: The radiomics model based on enhanced-CT images of pancreatic and peripancreatic regions performed well in the early prediction of AP severity. The nomogram based on selected radiomics features could provide a reference for AP clinical assessment.
      Cover page: Early prediction of acute pancreatitis severity based on changes in pancreatic and peripancreatic computed tomography radiomics nomogram.
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