Health Crisis and Housing Market Effects - Evidence from the U.S. Opioid Epidemic

According to the National Institute of Health (NIH), more than 130 people die in the U.S. every day from opioid-related overdose. Drug overdose is now the leading cause of accidental death in the United States, and 80% of individuals that are addicted to heroin were first prescribed opioids for pain relief. See Addiction Center (2020), National Institutes of Health (2020a) and National Institutes of Health (2020b) for further details.

For example, Florence et al. (2016) use data on health care claims and fatality cases to estimate the cost of the opioid crisis. The authors estimate that the cost of prescription opioid overdose, abuse and dependence in the U.S. was $78.5 billion in 2013.

See Washington Post (2019b) for additional details on the data.

The Washington Post notes the following regarding the data field: “DEA calculated field indicating number of pills, patches or lozenges, among others, shipped as part of the transaction.”

Drug Rehab (2020) notes additional information on drug potency.

See National Institutes of Health (2020c) for a complete list.

We implement some data cleaning rules. For instance, we trim the sample at the top and bottom percentiles of transaction price and change in price to account for outlying observations that may skew the results.

The standard errors are clustered at the zip-code level.

The standard deviations are noted in Table 3.

We perform several robustness checks and include the results in the Appendix. We use the opioid data over the entire study period and count the number of pharmacies within 2-miles of a repeat sales property to generate a standardized measure of the number of pills dispensed. There may be concerns relating to endogenous opening or closing of dispensaries influencing the results. Table 9 presents regression results based on a subset of transactions that did not involve any openings or closings. We note a similar inference as before. There may be considerable differences in areas that dispense opioids relative to those that do not. We repeat our analysis for the sample of repeat sales in high volume opioid areas, i.e., areas above the 25th-percentile of dispense (The main results involve repeat sales across all areas). Table 10 presents the regression results and we see that the overall inference remains unchanged. Lastly, we account for outliers due to either a very high or low amount of dispense by dispensaries or in specific areas by re-doing the analysis for a subset of repeat sale transactions that involve dispense between the top and bottom percentile of the distribution of dispense. The results are presented in Table 11 and the inference is robust to the exclusion of potential outliers.

The U.S. Census Bureau’s Small Area Income and Poverty Estimates program produces single-year estimates of income and poverty for all U.S. states and counties as well as estimates of school-age children in poverty for all 13,000+ school districts. The Local Area Unemployment Statistics (LAUS) program produces monthly and annual employment, unemployment, and labor force data for Census regions and divisions, states, counties, metropolitan areas, and many cities, by place of residence.

Washington Post (2019c) notes further details.

See Addiction Center (2020) for an additional discussion on opioid consumption leading to other drug addictions.