Data and algorithms used for housing prices collusion

A somewhat older news item. Politico and Ars Technica/Wired are among the outlets reporting about the RealPage price collusion case on the housing market. The heart of the matter is that this company is accused of allowing landlords to fix rental prices, using its software:

The Justice Department is gearing up to challenge what it says is collusive conduct in the rental housing market with a lawsuit against a software company that it believes allows large landlords to fix prices. (Quote from Politico)

The underlying issue already to be that the company’s software platform has become very good at aggregating public and non-public data, and recommending prices accordingly.

RealPage’s software is powerful because it anonymizes rental data and can provide landlords and property managers with nonpublic and public data about rentals, which may be different from that advertised publicly on platforms like real estate marketplace Zillow. The company contends that it’s not engaging in price-fixing, as landlords are not forced to accept the rents that RealPage’s algorithm suggests. Sometimes it even recommends landlords lower the rent, RealPage claims. But antitrust enforcers have alleged that even sharing private information via an algorithm and using it for price recommendations can be as conspiratorial as back-room handshake deals…(quote from the Ars/Wired article)

This appears another case where the mantra of ‘openness’ of data is not necessarily leading to desired outcomes for society at large. Instead of foster more inequality and weakens the position of renters. The unchecked amassing of data by companies fuels a market-driven logics in a domain where the product or service (housing) is more like a public good than a commercial one.

Perhaps these late fixes can help turn the tide?

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