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View Code? Open in Web Editor NEWA web service to assess the expected value of real estate under sea level rise
A web service to assess the expected value of real estate under sea level rise
Our web service call-and-response structure should correspond to industry standard, like the Zillow web services.
An example input set could be:
Parameter | Description | Default |
---|---|---|
address |
The address of the property to search. This string should be URL encoded. | none |
interest_rate |
Going interest rate | 5-year moving average |
climate_scenario |
S1-S9 climate change scenarios | scientific consensus |
api_key |
Each subscriber is uniquely identified by an ID sequence and every request to Web services requires this ID. | none |
The output should probably be JSON or even GeoJSON (with lat-lon point as spatial element). I actually like the idea that this should conform to the Playbook I've been working on with people from Mapbox and Esri.
This should serve to start a conversation on how we want to serve out our data.
@danhammer -- would you like me tackle this?
With a service that's functional (albeit incomplete and inaccurate) we can begin to assemble the pitch deck. Here are the broadly classified groups that we can approach:
Are there any good frameworks or software packages for generating and designing pitch decks?
Set up Travis CI for the repo. @harinisuresh is this something you'd be interested in doing? A compact way to engage. Mainly just reaching out to see if you still want to be active.
Per the error-handling in flask-reqparse.
Currently we are using Google App Engine (GAE) to provision the web service.
PROs
CONs
Other options
What have you used to build scalable web services?
@danhammer - Happy to tackle this or you can. I will have time this weekend (saturday/sunday)
A 2014 expert survey provides useful information for the estimation exercise: http://www.sciencedirect.com/science/article/pii/S0277379113004381.
RealClimate provides a detailed summary with tables and charts:
http://www.realclimate.org/index.php/archives/2013/11/sea-level-rise-what-the-experts-expect/
It's important to note that this survey was conducted before 2014, and potential SLR by 2100 is higher in recent publications (e.g. Hansen, et al. 2016):
http://www.atmos-chem-phys.net/16/3761/2016/acp-16-3761-2016.pdf
The 2014 exercise surveys 90 experts on relative SLR likelihoods for low and high warming scenarios in this century. It provides enough information to construct an expert-based probability function if relative likelihoods can be attached to the two warming scenarios.
I have searched for expert survey evidence on the relative likelihood of warming scenarios, but no luck so far. Relevant citations would be very helpful.
Just for a full featured service, based on something like this.
The value added of our web service is two-fold:
We have a few constituent REST services available. I will list the ones we have already here:
Esri geocoding service No API key required.
Zillow web service Note the API key in the query.
NOAA coastal sea level rise. This is an image server. Note that the returned value
field is sea level rise in feet.
This list in incomplete. Please list other data sets that you think would be helpful. For example, we still have to get data on the following:
Technical specs for this project, document.
We will ultimately estimate the value of a house under conditions of uncertain sea level rise. To do so, here are the resources we have access to:
We need to translate all of these items into an expected value. One suggestion is to calculate the time at which the value of housing at the given location goes to zero -- due to rising seas. There will be a distribution of end dates, with associated likelihoods. We should be able to convert this information into an expected present value of the house, based on the current valuation (presumably without taking into account sea level rise).
I am opening this issue to start the conversation about properly modeling the financials, given the environmental conditions we get from other data sources.
Consider this calculation. Suppose that the value of housing services is $100 per year. In 25 years, the same housing services are valued at $29.50. This can loosely be associated with the rent a given house can garner. The integral under this curve should equate to the current price of the house. If you stop receiving housing services after 25 years, what is the aggregate value of housing services from 25 years to forever relative to 0 - 25 years? This could be like a 15% reduction in value! That's worth a coastal landowner paying attention.
This should be written up. First, explaining the magnitude of value at risk. Second, the process we use to calculate that, given uncertainty in hydrology and uncertainty in sea level rise models.
We will begin to wrap up our current work into a pitch deck that we deliver to foundations, VCs, and social impact investors. I will use this thread to assemble content that will eventually be structured into a professionally designed pitch deck.
We may not have enough material to actually fund this project yet, but it would be good to test the waters with "friendlies" before investing too much more time into the tech.
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