Real estate data helps resource managers forecast water demand in cities

November 18th – Water resources managers and other policy makers only consider population, economic growth, and budget constraints when deciding allocations, but these factors cannot accurately predict changes in demand.

A new study published Wednesday in the journal Environmental Research Letters showed that scientists can use real estate data from online sites like Zillow to predict changes in home water usage.

A better understanding of changing water use and demand patterns will allow authorities to make more informed decisions about infrastructure planning, drought management and sustainability initiatives.

“Evolving development patterns can hold the key to success in building more water-friendly and long-term water security,” senior research author Newsha Ajami said in a news release. It was.

“Creating a water-resistant city in a changing climate is closely linked to how we can use water more efficiently as the population grows,” said Stanford University Western Program Urban Water Policy Officer. Director Ajami said.

Cities and suburbs continue to grow around the world, according to researchers, but not all growth is being produced equally.

The types of buildings that are being built to accommodate the growth of cities and suburbs, and the types of new buildings and people moving to the neighborhood, whether young families or retirees, are water as the population changes. Decide how to use it.

Sites like Zillow combine data from county and city agencies with information uploaded by local homeowners, so they are rich in the number and size of homes built and occupied near the city. It can be used as a source of information.

In a new study, researchers at Stanford University combine Zillow data with demographics from the U.S. Census to provide more details on the combination of people and buildings in the fast-growing and economically diverse city of Redwood City, California. I made a portrait.

When researchers analyzed the data using machine learning algorithms, they were able to identify five community groups or clusters.

Billing data from Redwood’s utility sector helped researchers identify both seasonal patterns and changes in water usage from 2007 to 2017 in five clusters. Researchers were also able to identify conservation rates during the historic drought in California from 2014 to 2017.

“The method of incorporating Zillow data allowed us to develop more accurate community groups than simply clustering customers based on income and other socio-economic qualities,” said lead researcher Kim Quesnel. Says.

“This more detailed view has led to some unexpected discoveries and provided better insights into the water-efficient community,” said Kessner, a postdoctoral fellow at the Woods Institute at Stanford University.

The data show that the two lowest-income clusters are characterized by high numbers of people per household, but by average levels of water usage.

The middle-income group used more water outdoors, but water usage in winter was below average, suggesting more efficient use of indoor equipment.

The water usage pattern diverged between the two highest income clusters. One cluster, characterized by young residents living in new parcels, showed the lowest water usage in Redwood City. Conversely, the wealthiest inhabitants of relatively large homes and large plots were the largest water users in the city.

All five clusters showed high water savings during the California drought, but the data show that California’s largest water users saved the most water.

Researchers suggest that their data analysis will help policy makers make better decisions about where to build new pipes and water treatment facilities.

Better data will also help authorities design more targeted conservation plans, they said.

“New accessible data sources give us the opportunity to gain a more informed understanding of water usage patterns and behaviors,” Ajami said. “Rethinking how cities of the future are built and how infrastructure is designed provides access to more equitable and affordable water across different communities.”

Real estate data helps resource managers forecast water demand in cities

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