So a zip code is more properly represented as an ordered point string or point cloud, rather than an area. There exist zip codes with up to 11 non-contiguous areas, zip codes which are valid only for the north half of an east-west street, and other oddities. You can fudge this a little by just drawing bounding boxes, but then you have up to 5 zip codes overlapping in some places.
That bit of trivia aside, they do make sense from a real estate point of view because a) all domiciles will have a zip code and b) it is usually freely available on listings.
Generally though, it's better to translate any given address to a census tract or census block group, since those divisions are actually defined as "a geographic region defined for the purpose of taking a census" - they can be properly mapped without ambiguity, crossovers, discontinuities, and other boggling features of zip codes, (though not of course with perfect regularity) and they also coordinate well with demographic data like median income - I'd be very interested to see a graph of percent of median income per square foot - I suspect you'd find some very 'hot' areas that don't seem like it.
The first hard algorithmic coding task I worked on was to generate isoclines of UK Postcodes from point cloud address data. Fortunately I didn't have to do the entire country, only a couple of million addresses, unfortunately it had to work on a machine running Windows 3.1 with only 4MiB of RAM....
The use of ZIP codes in GIS is largely a matter of convenience. The geocoding is generally good enough, particularly when using a statistically significant sample. And the underlying data are generally available as they're attached to most of the data of interest. That said, DIY geocoding with pretty high accuracy is possible and available:
Quick mash up adding plots for population density, % renter property and % owners (this took about 2 minutes - I was just intrigued how they all compared, which seemed especially relevant given the most recent XKCD).
It took me a while to remember that the Northwest side of Alameda is the naval base, and that's why it shows as white on both "occupied by renter" and "occupied by owner" maps.
The "occupied by renter" map confirms a lot about what I think about SF real estate, thanks
You can even see where it cuts off. See that little burnt orange stub at bottom center which sticks out into a sea of white? That's Central at Lawrence, where the SV/SC border goes nutty due to some annexing wars back in the '60s.
To answer the question posted in the post, no, I cannot find my zip or those of my friends, either.
Nice data, but nearly impossible to understand. It would be nice to put at least a couple of big cities (Palo Alto, Fremont, etc) on the map, so that you could understand where's what.
Under Debian, US Census TIGER files are available as dictionary files. You can use this to identify specific areas as you wish.
My analysis showed that the most expensive top several ZIP codes were SF Financial district, Stanford, SOMA, Marina, and Pacific Heights neighborhoods, as well as Atherton. I suspect the data may skew high, though it would help to have a distribution within each ZIP code as well (SD, median, quartiles, 5th/95th percentiles).
Does anyone know if any of the big real estate sites do this as a feature? It seems like zillow used to have this feature but they switched to a different map provider and could no longer offer it. I recently read a book about making money in real estate and the author suggested that you produce a heat map like this (by hand) for the area that you are interested in investing in.
I can't understand why this sort of thing isn't more commonplace. I would like to be able to create animated maps that show the prices over time. Or add in height to show the crime rate over time etc.
It seems like real estate is one of those industries with a strong lobby that has been able to resist having its information become a commodity.
When I bought my condo I thought the MLS was such a joke.
One suggestion: please further improve your startup's domain expertise w/r/t Manhattan (I don't know enough about the Bay Area).
Nobody I know in Manhattan - and I lived there over 15 years - thinks of South Street Seaport the way you describe it:
"The cobblestone streets combined with the low-density feel of the area give it a historical vibe that is second-to-none in the borough."
There are cobble-stone streets with low density and a historical feel in Tribeca, which - price aside - virtually all Manhattanites would consider significantly more desirable to live in than South Street Seaport.
South Street Seaport is a weird area to live. Very touristy. Far walk to most jobs on Wall St. Far from subways. Not that residential and very windy, since it is on the edge of the city.
I had one friend who lived in that area, on Cliff Street.
Sounds like typical real estate "enhanced truths" to me:
cobblestone streets: technically true
low density: nobody else wants to live here
historical vibe: the cobblestone streets make it look old
"It's a shithole for tourists in the middle of nowhere whose streets have been neglected by maintenance crews for decades," just doesn't sell apartments.
Price is only part of the story. Because of rent control laws in SF, prices aren't the signals of demand/scarcity they normally are. Showings for average apartments (in older buildings) in SF often have dozens of qualified applicants showing up, making them crapshoots.
It's surprising that Orinda/Lafayette are comparatively cheap now. They've been historically expensive places to live, they've each got good Bart stations, easy access, safety, and access to good food. But, people seem to ignore Lamorinda when looking for a place.
Well, the jobs/money are in the Valley and East Bay is a pretty long commute down there. If all the jobs were packed into SF, then everyone would live in the East Bay. East Bay is far nicer than much of San Jose & Silicon Valley IMO with the BART benefit.
It's pathetic that the technology hub of the world has piss poor infrastructure. It's a testament to the mediocrity of the California and US federal governments. The tax revenues generated there all flow outward to subsidize regressive states instead of investing into the place that shoulders much of the load of the economy.
$/sqft factored by apartment size results in a greater uniformity of rental pricing than would be immediately apparent. With few exceptions, the higher-cost areas will have smaller apartments generally catering to singles / childless couples, while families are driven to more to the periphery. Atherton and Stanford would be notable exceptions, as both include sizeable parcels (though how many of these are available as rentals is another question).
Parts of East Palo Alto are cheaper (but not necessarily somewhere you'd want to live...) but this is broken down by zip code, which isn't granular enough to show major differences (though you can see the Palo Alto area is very dark red compared to everything around it.
"ZIP codes designate only delivery points within the United States and its dependencies, as well as locations of its armed forces."
http://en.wikipedia.org/wiki/ZIP_code
So a zip code is more properly represented as an ordered point string or point cloud, rather than an area. There exist zip codes with up to 11 non-contiguous areas, zip codes which are valid only for the north half of an east-west street, and other oddities. You can fudge this a little by just drawing bounding boxes, but then you have up to 5 zip codes overlapping in some places.
That bit of trivia aside, they do make sense from a real estate point of view because a) all domiciles will have a zip code and b) it is usually freely available on listings.
Generally though, it's better to translate any given address to a census tract or census block group, since those divisions are actually defined as "a geographic region defined for the purpose of taking a census" - they can be properly mapped without ambiguity, crossovers, discontinuities, and other boggling features of zip codes, (though not of course with perfect regularity) and they also coordinate well with demographic data like median income - I'd be very interested to see a graph of percent of median income per square foot - I suspect you'd find some very 'hot' areas that don't seem like it.