earth-genome / coastal-valuation Goto Github PK

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:

1. Estimated value of the house, according to Zillow
2. Estimated rental rate of the house, according to Zillow, which presumably equates to the value of housing services each month.
3. The amount of sea level rise (according to the NOAA bathtub model) that would put the house under water at high tide.
4. The uncertainty associated with the value in Item 3. Just two categories: low or high uncertainty.
5. A probability distribution of sea level rise rates, collected from the best available science to date. This is rarely updated.

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.

Currently we are using Google App Engine (GAE) to provision the web service.

PROs

1. Simple to set up.
2. Highly scalable.
3. Easy integration with other Google Cloud services.

CONs

1. A lot of custom code and setup for GAE. difficult to swap systems, unlike a containerized architecture.
2. Unable to use all packages. Some are not supported by GAE, which means that for complex modeling, we probably can't use GAE.
3. I am not sure how easy it is to provision API keys and bill users.
4. Development environment is not trivial to set up. A lot of local deployment idiosyncrasies make it difficult for a reliable dev environment across machines.

Other options

1. Heroku
2. Kubernetes
3. ...

What have you used to build scalable web services?

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:

1. Venture capital. We have built a functional service for flood insurers and homeowners but need capital to ensure that local information is built into the service.
2. Flood insurers. Do you want to subscribe?
3. Reinsurers. (Zurich Re, Swiss Re) Do you want to subscribe?
4. Housing web apps. (Zillow, Redfin, Trulia, MLS) We want to install our home value service on your web application. [Do we tell them about the follow-up to purchase additional information about projected changes to their mandated flood insurance rates?]

Are there any good frameworks or software packages for generating and designing pitch decks?

@danhammer - Happy to tackle this or you can. I will have time this weekend (saturday/sunday)

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.

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.

Per the error-handling in flask-reqparse.

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.

Our web service call-and-response structure should correspond to industry standard, like the Zillow web services.

An example input set could be:

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.

Technical specs for this project, document.

The value added of our web service is two-fold:

1. Expert delivery of environmentally based information.
2. Interpretation of multiple raw data streams into a single, financial metric.

We have a few constituent REST services available. I will list the ones we have already here:

1. Esri geocoding service No API key required.

   example: geocode.arcgis.com/arcgis/rest/services/World/GeocodeServer/findAddressCandidates?SingleLine=380+New+York+Street,+Redlands,+CA+92373&f=pjson

2. Zillow web service Note the API key in the query.

   example:
   www.zillow.com/webservice/GetSearchResults.htm?zws-id=X1-ZWz19l1vnzxtzf_ac8os&address=2114+Bigelow+Ave&citystatezip=Seattle+WA

3. NOAA coastal sea level rise. This is an image server. Note that the returned value field is sea level rise in feet.

   example:
   http://earthgenomevm.cloudapp.net:6080/arcgis/rest/services/SLR_Depth_6ft/ImageServer/identify?geometry={x:-79.91985,y:32.862202}&geometryType=esriGeometryPoint&returnGeometry=false&returnCatalogItems=false&f=pjson

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:

1. Climate change scenarios and the associated sea level rise
2. Real interest rates over time

@danhammer -- would you like me tackle this?