House and apartment rental prices are influenced by various factors. However, a good rental price predictions model analyzes the different features of a home and its surroundings to generate the most suitable rent price.
Homeowners, real estate brokers, and investors all need accurate rent prices; a good rent price predictions model generates these prices. Real estate agents or online real estate websites then show these optimal results to clients, making renting easy, no matter the apartment type, location, or features.
Important internal data for a rental price prediction model abound. Some examples include property characteristics like number of bedrooms, bathroom size, parking spaces, and square footage. Other examples are geographic, such as property location, area type, and distance from points of interest like hospitals, colleges, schools, etc.
External data for a good model include market conditions and trends like unemployment level, average income, crime rates, and historical rent trends. Additional data includes school rankings, and neighborhood features.
Analyzing data in other rental markets such as short-term rentals may prove useful for a rental price prediction model. For example, short-term rental properties moving into the long-term rental market will affect the rent in general.
Housing and rental price prediction is such a complex endeavor with so much relevant data that it was the perfect choice for the Data Science Challenge @ EEF 2019 held by the Engineering Education for the Future (EEF) organization. Turning such a difficult challenge into a productive business model creates one of the greatest challenges of this use case.
As we head into late summer, tenants are beginning to request lease modifications and concessions, but rents have yet to falter. At some point that will change, but predicting where rents are heading is challenging, largely because there is a double market with essential and non-essential tenants. But, there are other factors as well that make predicting retail rental rates—and in turn retail investment—a challenge.
Rent in rented dwellings by region, rental data, observations and year
Rent in rented dwelling by districts, rental data, observations and year
Rent in rented dwelling by region, number of rooms, rental data, observations and year
Rent in rented dwelling by number of rooms, year of construction, type of ownership, rental data, observations and year
Rent in rented dwelling by region, number of rooms, type of ownership, rental data, observations and year