Robert Kurtzman

We consider official and alternative data sources for measuring and projecting the role of U.S. manufacturing in the global semiconductor manufacturing industry. We find that the government sources we consider face significant limitations for tracking semiconductor fab investment in real time: they use industry and product categories that are too broad and come with substantial publication lags. We then analyze project-level alternative data from Semiconductor Equipment and Materials International (SEMI), which provides quarterly tracking of individual fab projects with greater timeliness and specificity. Comparing SEMI data with official statistics through 2023, we find broad consistency in investment levels, validating SEMI as a credible supplement to official statistics. This work provides both a practical guide for tracking real-time trends in semiconductor manufacturing and a case study in synthesizing government and alternative data sources.

Place-based policies are often caught between two potentially conflicting aims: (i) directing aid to needy communities and (ii) spurring investment. We study this tradeoff in the context of the Opportunity Zones (OZ) program. Leveraging unique phase-level microdata on commercial construction projects, we show that US state governors prioritized designating tracts where construction projects were already being planned. About two-thirds of the greater construction growth in OZs can be attributed to this selection. States prioritizing tracts with greater investment opportunities observed larger construction increases in designated tracts. We calibrate a structural model to quantify the effects of the program and examine counterfactuals under alternative preferences or eligibility criteria.

Data center investment in the U.S. has increased rapidly in the post-pandemic era, and plans for future investment have surged further. Forecasting investment at such a turning point is an important but potentially fraught exercise, especially given lags in aggregate data availability. We develop a straightforward method to forecast aggregate investment using project-level microdata and a small number of parameters: specifically, abandonment rates, time from planning to start, and time from start to completion. As a key validation of our approach, we generate estimates that match the recent history of aggregate data center investment in the NIPAs. We then use our method to generate nowcasts of aggregate data center investment in the short run, with the mean forecast indicating that investment will increase to $370 billion annualized by 2026:Q2. We can extend our methodology further out, but our forecasts then become conditional on the assumed flow of new data center plans. Assuming future plans range from one-fourth to twice the average pace of plans from 2024-2025 implies a range of investment forecasts of $360 billion to $930 billion in 2027, demonstrating the substantial upside and downside risks to future levels of investment.

We use microdata on the phases of commercial construction projects to document three facts regarding the sector's time-to-plan lags: (1) plan times are long and highly variable, (2) nearly half of projects in planning are abandoned, and (3) property price appreciation reduces the likelihood of abandonment. We write down a tractable model of endogenous planning starts and abandonment that can match these facts. The model also has the testable implication that supply is more elastic when there are more "shovel ready" projects ready for construction. We use local projections to validate this prediction in the cross-section for US cities.

Manufacturing structures investment is the largest component by value of U.S. nonresidential structure investment and has been growing quickly in the post-pandemic period. Figure 1 documents the significant extent to which the construction of manufacturing plants has contributed to growth in overall nonresidential construction. The surge in investment in manufacturing construction has occurred despite the higher interest rate environment, driven by government incentives associated with the Inflation Reduction Act (IRA) and the CHIPS and Science Act (CHIPS), and market forces such as increased demand for semiconductors and batteries and the re-shoring of supply chains.

Before examining trends in lending, it is useful to first examine trends in the financial assets of the institutions more generally.

A significant share of commercial real estate (CRE) investment properties—about half by our estimates—are purchased without a mortgage. Using comprehensive microdata on transactions in the US CRE market, we analyze which types of properties are purchased without a mortgage, highlighting the important role of renovation or redevelopment options. We show that mortgage‐financed properties are less likely to be subsequently redeveloped, and that owners anticipate these redevelopment frictions and avoid mortgage financing for properties with greater redevelopment options. These effects were even stronger during the COVID‐19 pandemic, when uncertainty increased redevelopment option values.

Rising interest rates and structural shifts in the demand for space have strained CRE markets and prompted concern about contagion to the largest CRE debt holder: banks. We use confidential loan-level data on bank CRE portfolios to examine banks' exposure to at-risk CRE loans. We investigate (1) what loan characteristics are associated with delinquency and (2) to what extent the portfolio composition of major CRE lenders determines their exposure to losses. Higher LTVs, larger property sizes, and greater local remote work tendencies are all associated with increased delinquency risk, particularly for office loans. We use several machine learning algorithms to demonstrate that variation in exposure to these risk factors can account for most of the performance disparity across different types of CRE lenders. The headline result is that small banks' comparatively modest delinquency rates mostly reflect observable portfolio characteristics---predominantly their low holdings of large-sized office loans---rather than unobserved factors like extension or modification tendencies.

A significant share of commercial real estate (CRE) investment properties—about half by our estimates—are purchased without a mortgage. Using comprehensive microdata on transactions in the US CRE market, we analyze which types of properties are purchased without a mortgage, highlighting the important role of renovation or redevelopment options. We show that mortgage-financed properties are less likely to be subsequently redeveloped, and that owners anticipate these redevelopment frictions and avoid mortgage financing for properties with greater redevelopment options. These effects were even stronger during the COVID-19 pandemic, when uncertainty increased redevelopment option values.

A significant share of commercial real estate (CRE) investment properties---about half by our estimates---are purchased without a mortgage. Using comprehensive microdata on transactions in the U.S. CRE market, we analyze which types of properties are purchased without a mortgage, highlighting the important role of renovation or redevelopment options. We show that mortgage-financed properties are less likely to be subsequently redeveloped, and that owners anticipate these redevelopment frictions and avoid mortgage financing for properties with greater redevelopment options. These effects were even stronger during the COVID-19 pandemic, when uncertainty increased redevelopment option values.