Housing prices are a little bit of a problem in California.
Right now, the average home value in San Francisco is north of $1 million. Of the country’s 100 most expensive zip codes, 77 are in California. The state is decades behind on building enough housing stock to meet demand, and lawmakers are having trouble finding ways to fix it.
It’s that last component that has Yury Lifshits and Stepan Korshakov intrigued. They have been looking into housing data in San Francisco for about half a year, and quickly found some problems: Government data sets containing information relevant to housing policy are often disconnected, housed in various departments and contain discrepancies on things as fundamental as how many apartment units are in a single building.
“There were just mistakes because people are in charge of entering the data and they sometimes enter the wrong number,” said Lifshits.
How, he asks, can regulators and policymakers fix the housing crisis if they can’t answer questions about the housing market?
They’ve started a company to try to help government do just that. It’s called Statecraft, and it’s enrolled in the Y Combinator startup incubator program’s Winter 2018 cohort. For the time being, they’re targeting customers like city and county planning departments, councilmembers, supervisors and their policy advisors.
The company has a lot of plans in the future, according to Lifshits, the chief executive officer. But for right now, it’s focusing on housing, and three topics in particular: reporting, opportunity sites and policy insights.
When it comes to policy, Lifshits thinks the people in charge will be able to work more effectively if they’re able to draw insights from multiple data sets at once. For example: A city might require developers to set aside a certain percentage of units in new apartment buildings for low-income residents. But how do developers respond to that? Has the law resulted in a greater percentage of affordable units per building, but fewer affordable units overall due to reduced development activity?
It will take different types of data to answer that question.
“Right now, no one knows which zoning code generates the most property taxes, for example … or which zoning code generates the most affordable housing,” Lifshits said.
Ideally, insights like that would shine a light for policymakers to learn from mistakes, identify successes and pursue the best paths in the future.
Data integration, normalization and quality analysis will be a big component of that. For example, if two city departments have a different count for the number of housing units in a building, how can policymakers know which one is right and get an accurate count of units built in a given timeframe?
“You want to bring together the multiple data sources and then join them by using some universal identifiers for buildings,” Lifshits said.
Then, the company can figure out which count is accurate — eventually, perhaps, they could even set up a user interface for developers to enter that information themselves.
He also wants to gather information on the application process, and how long it takes for cities to approve building permits. That way, they could identify what a reasonable benchmark looks like for approval times and begin to look for ways to speed up the process.
What that all would mean, for local government employees who deal with these issues, is streamlined reporting. By setting up streamlined processes for gathering data on multiple pieces of the larger housing picture, officials who need to report information to others — the U.S. Department of Housing and Urban Development, for example — will be able to do so more quickly.
Another service Statecraft will offer is digitizing and publishing zoning maps, which Lifshits said are often confined to PDFs. By putting those maps online, making them interactive and giving users ways to overlay other kinds of data on top of zoning rules, he thinks the average person will be much better able to understand the rules that govern changes in their city.
Tied into that work is an effort to build lists of sites available for development — a market research tool central to certain kinds of city work. For example, Lifshits said, cities and housing authorities which have money to build affordable housing or transit-oriented development projects must compete with private developers who want to build on the same land.
“They compete for the same kind of opportunity sites,” he said. “And at the moment, market rate realtors have the advantage because they have dedicated people to find those sites, and they have an advantage in capital and things like that.”
The company is in talks with several cities, counties and other organizations at the moment to start working for them, but isn’t announcing any clients or pilot projects yet.
The company’s co-founders are both Russian-born, having moved to the U.S. after studying at universities in their home country. Lifshits has attained American citizenship and lived in the country for more than a decade, working at Yahoo! before going on to lead several companies.
Housing is just the first focus area for Lifshits and Korshakov. They’re also interested in using data to help local government address all kinds of urban problems, from homelessness to tax collection.
“The purpose of the company is to make cities more efficient,” Lifshits said.