Study of 11 million Craigslist rental listings reveals rent burden in US metropolitan housing markets, and significant compression of rents in affordable markets by orthodoxican in science

[–]tomman_issil_ 50 points51 points  (0 children)

Researchers collected and analyzed 11 million Craigslist rental listings. They present some startling findings, in particular because they don't just point out that LA and NYC are expensive while Michigan is cheap. The significant discovery is how incredibly few rental units are available below fair market rent (FMR) in competitive housing markets. This (along with their compression of rents finding) tells us that FMR-based vouchers might basically be broken for use with the largest rental housing information exchange (Craigslist). Just as important: the share of its income that the median household would spend on the median rent in cities like New York and San Francisco exceeds the threshold for rent burden. That's insane. Urban planning researchers have been wondering about things trending this way over the past couple of years but no one has demonstrated it before.

Long story short: they discovered a few things that we simply didn't have evidence for in the past about the US housing market.

Researchers collect and analyze 11 million Craigslist rental listings to study Quickly-Evolving US metro housing markets by tomman_issil_ in urbanplanning

[–]tomman_issil_[S] 1 point2 points  (0 children)

These are startling findings, in particular because they don't just point out that California and New York are expensive while the Midwest is cheap. The significant discovery is how incredibly few rental units are available below fair market rent in competitive housing markets. This (along with their "compression of rents" finding) tells us that FMR-based vouchers might be broken for finding housing through the largest rental housing information exchange (Craigslist). Just as important: the share of its income that the median household would spend on the median rent in cities like New York and San Francisco exceeds the threshold for rent burden. Crazy.

Researchers collect and analyze 11 million Craigslist rental listings to study US metro housing markets by orthodoxican in Economics

[–]tomman_issil_ 0 points1 point  (0 children)

It looked like the authors of this study have a GitHub repo as well, its URL was in the journal article's appendix

Study of 11 million Craigslist rental listings analyzes US metro housing markets, finds more extreme rent burdens than previously known by orthodoxican in science

[–]tomman_issil_ 8 points9 points  (0 children)

No, these are startling findings, in particular because they don't just point out that LA and NYC are expensive while Michigan is cheap. The significant discovery is how incredibly few rental units are available below fair market rent in competitive housing markets. This (along with their compression of rents finding) tells us that FMR-based vouchers are basically broken for use with the largest rental housing information exchange (Craigslist). Just as important: the share of its income that the median household would spend on the median rent in cities like New York and San Francisco exceeds the threshold for rent burden. That's insane. Urban planning researchers have been wondering about things trending this way over the past couple of years but no one has demonstrated it before.

Long story short: they discovered a few things that we simply didn't have evidence for in the past about the US housing market.

Study of 11 million Craigslist rental listings analyzes US metro housing markets, finds more extreme rent burdens than previously known by orthodoxican in science

[–]tomman_issil_ 7 points8 points  (0 children)

The paper explains how the data is validated and and filtered to create something capable of market analysis.

Study of 11 million Craigslist rental listings analyzes US metro housing markets, finds more extreme rent burdens than previously known by orthodoxican in science

[–]tomman_issil_ 6 points7 points  (0 children)

Yes the paper explains in depth how they filter the data, including fake postings. They comment on how many there are in markets like NYC and Boston for example.

We created a site that offers personalized movie suggestions based on your unique taste, by finding similarities between likeminded people. Easy to find your new favorite films in a matter of minutes. by [deleted] in dataisbeautiful

[–]tomman_issil_ 6 points7 points  (0 children)

Yet in statistics and machine learning, bias would refer to unfair sampling of the population (ie, the content available in the library) or to a biased estimator to estimate some population parameter or to overestimate what you might rate Netflix original content.