REPORT: COVID-19 Hotel Forecast - San Francisco

San Francisco mayor London Breed took an early, aggressive stance on social distancing, declaring an emergency on February 25, nine days before the city recorded its first confirmed COVID-19 cases.

Despite being criticized at the time for overreacting, Breed’s strategy appears to have successfully flattened the curve for San Francisco – a densely populated city with a sizable homeless population.

Flattening the curve is essential to avoid unnecessary stress on healthcare resources and to protect the welfare of the local community. However, for the lodging industry, strong social distancing measures and stay-at-home regulations have a catastrophic effect on operations and profitability.

In early March, prior to its first COVID-19 cases, the forecasting model predicted a 9% decline in the city’s year-over-year (YoY) Revenue per Available Room (RevPAR) variance for the month of April. Six weeks later, the April 12 model has reforecast the YoY RevPAR collapse to -97% for the month.

As of April 20, San Francisco County (population 880,000) has recorded 1,157 confirmed cases (0.01% of the population) and 20 deaths (1.7% of confirmed cases). San Francisco’s testing rates are also the highest among all major California counties.

While delivering commendable public health outcomes is of the utmost importance, it is of little consolation when viewed solely through the prism of near-term business prospects for hoteliers. The lockdowns save lives and suppress hotel occupancy.

Our previous forecasts in Singapore and New York City clearly illustrated the inverse correlation between confirmed virus cases and weakening of hotel demand. The San Francisco market exhibits the same “X” pattern, even though the case counts have been much more muted in real numbers.

Over the coming months, by teaming up with the data science team at LodgIQ, Phocuswright is evaluating a broad swathe of hotel-related and other data across a variety of key metropolitan areas.

Our key objectives are to model the:

Level of disruption

Duration of disruption

Shape of the recovery curve

The goal is to understand the similarities and differences in hotel market dynamics between destinations.

This is especially relevant, as some markets may have yet to peak in terms of the level of infections, while others are seeing active coronavirus case counts decline.