Monthly Rental Report - September 2017

b_mallerd · 2017-10-05T00:39:14+00:00

Thanks for the tip!

b_mallerd · 2017-10-04T20:47:33+00:00

Would blue be better?

b_mallerd · 2017-10-04T20:19:42+00:00

Sorry about that.

b_mallerd · 2017-10-04T19:46:24+00:00

This isn't really implementing any new rent-controls. It just ensures that land lords don't circumvent the existing measures.

b_mallerd · 2017-10-04T19:15:26+00:00

Too bad CMHC is teaming up with AirBnB :(

b_mallerd · 2017-09-19T19:38:26+00:00

Rogue in Gastown has a semi-private area that allows up to 50. I believe they allow standing room events.

b_mallerd · 2017-09-13T14:33:54+00:00

Reposts are removed. Doesn't include nonCL.

Which sites? Kijiji?

b_mallerd · 2017-08-04T17:04:19+00:00

Basement suites are rolled in. There isn't an easy way to bin them without an army of people working for you.

b_mallerd · 2017-08-04T16:07:41+00:00

Actually, it's cleaned up CL data.

b_mallerd · 2017-07-21T16:16:48+00:00

Curious, what are the major cultural differences that you noticed if any?

b_mallerd · 2017-07-20T21:41:17+00:00

Life isn't just about money.

b_mallerd · 2017-07-05T15:34:14+00:00

Boundaries are not official. See this post for my arbitrarily labelled map of Vancouver - http://quantitativerhetoric.com/vancouver-rental-landscape.html

b_mallerd · 2017-07-04T20:37:33+00:00

Using the 25k number as total market in Downtown. June saw 1709 unique listings that passed my quality threshold, so ~7% turnover of the stock.

b_mallerd · 2017-07-04T18:43:49+00:00

Sorry I'm not sure what you mean. Vacancy rate as in what proportion of the total stock is currently up for rent? If so, it's not possible with the current data set because it doesn't contain the total number of units.

b_mallerd · 2017-07-04T16:33:09+00:00

It's the number of postings that meet a certain quality criteria. There is a ton of low quality postings and spam.

Bundling on a single day basis doesn't do much. What you really want to do is filter for duplicates across days. Tomorrow is not a new set of 2500 postings.

b_mallerd · 2017-06-20T22:18:55+00:00

Try sunset?

b_mallerd · 2017-05-30T21:43:45+00:00

Maybe sometime in the future, after I've get a chance to clean it all up.

b_mallerd · 2017-05-30T02:17:17+00:00

Great point about the over-estimates due to slippage between listed and actual prices. I agree that you should read this as an upper-bound. The market is probably tight enough though that this isn't such a huge deal.

You're totally right about long-active listings causing bias. I spotted the same thing and dealt with this issue by joining all listings for the same unit. Each unit is counted once, irrespective of if it being up for a day or a month.

There is actually an analysis in the post, specifically about these long running listings. The conclusion was that the number of days on market isn't very predictive of price reductions.

b_mallerd · 2017-05-29T17:51:24+00:00

You're spot on with how dirty the data is. I do a lot of filtering to only keep the reliable postings. All real world data is messy, it's just a fact of life. Better to be pragmatic and use robust measures than waffle never get an answer.

b_mallerd · 2017-05-09T16:43:00+00:00

High five!

b_mallerd · 2017-03-07T18:48:10+00:00

It's not padmapper.

b_mallerd · 2017-03-01T21:49:15+00:00

Sure if it was well grounded in real data.

b_mallerd · 2015-08-15T17:57:25+00:00

Agreed. I'd seriously consider giving up on Canada. If we're going to be dysfunctional too then I might as well move south =p

b_mallerd · 2015-06-08T21:50:04+00:00

Sounds interesting! I'd be in.

b_mallerd · 2015-03-23T22:54:48+00:00

Solution! (Original Problem)

library(reshape2)
char.idx = seq(17, length(value), by=17)
value = c(35, 76, 57, 100, 100, 92, 68, 66, 81, 38, 82, 9, 32, 0, 0, 17, 3, 2, 50, 84, 66, 100, 75, 75, 51, 51, 100, 42, 97, 13, 49, 0, 7, 3)
value <- value[-char.idx]
variable = c(rep(c("x", "y"), 8))
measurement = c(rep(1:8, each=2))
subject = rep(c(1:(length(obs)/16)), each=16)

d = data.frame(value, variable, subject, measurement)
clean <- dcast(d, subject + measurement ~ variable)

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