The Loveland Blog

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Updated 2020 Puerto Rico data now on Landgrid.com

By Sahana Murthy on April 28, 2020 · Announcements

Big Announcement: Updated Puerto Rico data from 2020, now available at Landgrid.com.

We are thrilled to announce that we now have the most recent, 2020 data available for Puerto Rico. 

Well, we’ve had Puerto Rico data for some years -- and helped develop a plan and budget for a community-driven island-wide property survey that could still come to life one day if the time is right -- however, since 2016, we were unable to get updated, refreshed data for the island.

Not anymore! We recently received the most recent & updated data for all of Puerto Rico for 2020. That’s right - straight from a PR source. 

We have added 38,635 additional parcels for Puerto Rico to our dataset: that brings us up to 1,523,802 parcels in total.

If you're looking for the latest data for all of Puerto Rico, and we know many of you are since we get asked for this a lot, you can see it on the site at https://landgrid.com/store/us/pr.

If you need to export the raw data for use outside the site, email us at parcels@landgrid.com. It’s available for download and via API. 

It is also available for self-serve download by the municipality on the Landgrid parcel data store: https://landgrid.com/store/us/pr

Loveland team members have made numerous visits to Puerto Rico and developed some good friendships on the island since Maria. We look forward to being back when the pandemic passes and it's safe to travel, and we hope to be helpful on more local projects in the future.

 

Below, find some notable improvements & updates between the data we had in 2016 and the data we have now:

Attribute/Column Name

Data from 2016 

NEW PR Data (2020)

Ll_uuid (total number of unique parcels)

1,485,221

1,523,802

Parcel number

1,473,852

1,467,502

Mailing Address

Didn’t exist.

715,569 (Woo Hoo)

Parcel Address

1,263,592

1,301,223

Owner

737,183

1,301,821

Last Sale Date

Didn’t exist.

824,960

Hurricane Maria - Brief Analysis:

Our latest data update for PR also gave us some key insights into the possible impact of Hurricane Maria on the island’s property landscape - since Hurricane Maria hit Puerto Rico on Sept. 20, 2017, we have one dataset prior to the hurricane and one after.

Please bear in mind that the data we discuss below can NOT be directly a result or impact of Maria (although speculatively, they could very well be). It's also not possible for us to know the details of how the data is updated and maintained, and what might be missing that would add additional insight or otherwise impact the conclusions we draw below. We share some observations from the data here to help prompt further research and greater understanding of how things are changing over time.

 


Topo map, rainfall amounts, and Hurricane Maria’s path across Puerto Rico. Image from USGS. ^

 

Non-PR Ownership

A 'mailing address' is the place of record where an owner wants information (tax bills, other correspondence to property owners from the municipal government) to be sent. This makes it interesting data for analysis.


properties with non-Puerto Rico mailing addresses ^

  • About 9% of Puerto Rico’s parcels for which we have owner mailing info list a non-PR mailing address currently. There are a further 1,600 properties that list a P.O. Box as the mailing address, for which location is difficult to determine.

    It’s worth noting here that it’s not possible to break out how much of this ownership is former Puerto Rico residents who now live abroad, and how much represents external investment / non-Puerto Rican ownership.

    Top 5 states with non-PR mailing addresses:
    Florida: 9,162
    New York: 8,756
    New Jersey: 3,294
    Texas: 2,617
    Massachusetts: 1,448

  • There were 4,064 purchases in the 2.5 years since Maria (ie from Maria until March 2020) that list a non-PR owner mailing address. For context, there were 3,081 out-of-state purchases in the 2.5 years prior, showing a 24% increase since Hurricane Maria.


Sale Data (Pre & Post Maria)
In an attempt to compare apples to apples as much as possible within this quick analysis, we took Hurricane Maria as the inflection point. Since we’re just about 2.5 years after Maria as of this writing, we took a corresponding 2.5 years prior to Maria (March 2015). All post- and pre- Maria data below, unless otherwise noted, are working with this 2.5 year span on either side.

  • In terms of overall sales numbers, we see a 10% decrease in volume:
    pre-Maria: 65,792
    post-Maria: 59,324


  • There was a corresponding decline in sales greater than $100,000:
    pre-Maria: 21,446
    post-Maria: 19,248



Sales pre-Maria over $100,000


Sales post-Maria over $100,000


  • In general, median sales prices fell in Puerto Rico after the hurricane. One exception was the San Juan area, which saw a rise in prices. While one might think that this market activity in San Juan might be related to outside investment, we found nothing to indicate that that was the case: sales in that area were no more likely to have non-PR addresses than on the island as a whole (right around 10%).




There's much more to be learned from this dataset. If our software or the data itself can be helpful to your work, research, or advocacy in Puerto Rico please email us at team@landgrid.com.

- The Landgrid Team

White Labels & You

By Nick Downer on April 21, 2020 · How-To

Hey all, this is Nick with Landgrid. I thought I’d do a quick blog post to talk a little bit about how excited we are to be able to offer white labeling services to our clients, and explain why we’re so stoked.

To Start:
You already know that Loveland makes fantastic GIS software for visualizing and managing property information out of the box via our flagship platform, Landgrid.com. However, many of our most powerful projects have customized the look and feel of our software to the exact needs of a business, government, or community development organization (the Flint Property Portal is a great example).




In the past these projects were one-offs, meaning that the cool features that we made for one client we couldn’t easily replicate for others. What we have now for the first time is the ability to create these for pretty much any Enterprise customer who would like one, at scale. 

 

What this means:
No matter what you’re using our platform and/or data for, we can customize a site that’ll make it all about you and your needs. We can change the look and feel of the site visually, as well as highlighting the information you need and removing anything you don’t. If you’ve ever thought “I wish I could do X thing with this data”, the time has come - now you can. I think you’ll agree, that’s pretty compelling.

So, what exactly is a white label? Glad you asked, and here’s a handy graphic to help explain.




As you can probably gather, white labeling is a broad concept and can apply to a lot of potential use cases, which is one of the reasons I think it’s so cool. For existing clients, white labeling can really supercharge the impact, utility, and visibility of their work with Landgrid; in addition, I also anticipate a whole swath of potential new customers for whom we suddenly become relevant and attractive.



Rather than continuing in generalities, I thought I’d lay out some specific examples of things that white labeling now allows us to get into via some friendly hypotheticals. If any of these spark an idea for you, your business, or someone you know, please check out our revamped Enterprise page and shoot me an email at enterprise@landgrid.com. As always, we’re more than happy to talk through the details as they relate to you specifically - we're a friendly bunch and love to take customers all the way from concept to finished product.


Real Estate
Scenario 1
You’re a real estate company with a portfolio of properties which you hold and/or manage. Your investors want to be able to quickly and easily see which properties are owned, status updates as they occur, and track potential future investments.
Solution
With a white labeled site, you can give your investors access to a private site at a URL of your choosing, with your companies’ branding, that color-codes properties by ownership or as potential leads. Property managers during their daily work or staff scoping out potential acquisitions use the Landgrid app to snap photos and make notes about properties, all of which shows up in real time on a customized field.

Scenario 2
You’re a realtor whose primary interest is in a specific region (or regions) of the country. You want the latest property transfer data as well as any other metrics you can get your hands on that will help you determine which properties to take on and how to market them.
Solution
We’re bulking up our customizations so that you could set up a white label solution that pulls the metrics you’re interested in onto an Insights dashboard. You could see the number and sales value of properties sold in an area over time, bookmark properties you want to check out, and set up a data integration so that other sales data you have access to flows into your Landgrid account automatically.

Scenario 3
You’re a real estate investor, and you specialize in certain types of properties (commercial, vacant lots, residential rehab, etc). You need access to data that will give you an edge on finding these properties before your competitors.
Solution
Here, we’d set up some customizations on an Insights dashboard that could flag properties that fit your criteria as soon as they become available, in addition to some premade maps that would (for example) color code vacant commercial structures as yellow, vacant residential structures as blue, etc. Using this information, you could either go out and visit them in person with our survey app, or use it as a starting point to do outreach to the owners of those properties or other research.

Land Bank
Scenario 4
You’re a land bank, and you need a way to track properties from abandonment and vacancy, through acquisition, all the way to sale and transfer to new owners. You have hundreds of these properties, and it’s becoming a huge hassle to keep track of them all and make sure you have the most current data for any given property!
Solution
First of all, if you were using a database software to track properties, we could integrate with that - so that changes you make there get reflected onto our map. If you were doing it more, let’s say, “old school” via paper or a simple spreadsheet, we would be happy to consult, design and set up a new workflow for you that uses Landgrid primarily, making far more efficient use of you and your staff’s time going forward.

Nonprofit/Government
Scenario 5
You’re concerned with the increasing number of tax delinquent properties in your area, and you need segmented data, as well as a way of tracking outreach to residents.
Solution
We could set you up with a custom site that would have a handful of premade maps: one highlighting tax delinquent properties, one highlighting properties that have had outreach (what, when, and status), and a handful of other premade maps showing things like valuation and transfers so that you can anticipate which areas to pay special attention to in the coming months.

Scenario 6
You're a municipal government looking for a user-friendly way to publicly show the various investments and projects being undertaken by the city, because you value transparency and open data.
Solution
We’d set up for you an attractive public-facing interface as well as the training needed for staff to input data; premade maps for each area (streetscape improvements, paving projects, tree planting, etc) could be created. All data could be integrated with an existing Open Data portal if it exists for easy resident download, and/or we could host those downloads on the white labeled site itself.

Academic
Scenario 7
You’re digitizing hard-copy historic data (Census, building records, sales data, photos, etc) and you need a platform to input this data and tie it to the current parcel. You want the final product to be available to other researchers in the future.
Solution
Rather than recreate the wheel, you can use a site with Landgrid’s parcels as a georeferenced base on which to append your data. This site can be used by any number of staff while digitizing to submit records, and once the project is complete, you could work with Landgrid staff to visualize the data in whatever way feels most compelling. Project leaders could view productivity stats for staff working on the project, review digitized records, and flag any in need of further review.

Philanthropic
Scenario 8
You want a dashboard that can track investments you’ve made in a given city, or nationwide. In addition, you want to be able to easily see specific information that might inform data-driven decisions around equitable funding, and identify existing gaps in the funding you provide.
Solution
We can create an attractive interface that you can use to generate needed metrics for funders quickly ("we have funded 20 projects in ZIP code XYZ, 3 of which were arts & culture grants, and invested $3m") on a map which can be private or public.

Parcel Data Update - April 2020

By Sahana Murthy on April 10, 2020 · Democratizing Data

A summary of updates in March of 2020 and the upcoming pipeline is below. 

April 2020 - Key Data Stats 
Current average parcel age  - 235,    down from 257 last month 
Current average county age - 310,  down from 350 last month

Q1 2020 - Key Data Stats 

Number of counties refreshed:


Q1 2020: 929 counties

Q1 2019: 610
Q2 2019: 454
Q3 2019: 292
Q4 2019: 722

The Landgrid Data Store: We recently launched the data store, to allow our customers to quickly buy county data on the go. Most of you have our nationwide & statewide data with updates. However, if some of you are interested in individual county data or a handful of counties, you can now just go straight to the data store and buy data by the county, hasslefree and without delays. 
https://landgrid.com/store

SPECIAL NOTE:  USPS Vacancy, Residential indicators:
Updated in March 2020.

Coverage Report: Updated for this month and available here: 
https://docs.google.com/spreadsheets/d/1q0PZB72nO8935EMGmsh3864VjEAMUE-pdHcPkoAiS5c/

For all full dataset customers, the updated data is available for download to bulk data clients in these formats: GeoPKG .gpkg (suggested), GeoJSON, Shapefile, and Postgres SQL files.  In addition, this data has been updated on the landrid.com website.

If your organization uses a custom export we are updating your data at the moment and if you don’t see the latest updates, please drop us a line.

A Data Dictionary for the Loveland Standard Schema is always available here:
https://docs.google.com/spreadsheets/d/14RcBKyiEGa7q-SR0rFnDHVcovb9uegPJ3sfb3WlNPc0/

A machine-readable version of this list is included in the `verse` table available in all the formats above as well as CSV format for use in spreadsheets. To find the latest updates in verse, sort by 'last_refresh' and use the 'filename_stem' column to identify the file.

Data updated or added from the county in March and live now:
(bold indicates a  newly added county)
--------------------------------------------------
California - San Diego

Hawaii - Hawaii, Honolulu, Kalawao, Kauai, Maui

Iowa - Statewide, all counties refreshed

Illinois - Ford

Kansas - Jewell, Sedgwick, Washington

Michigan - Wayne

Mississippi (7 newly added) - Adams, Alcorn, Amite, Attala, Benton, Bolivar, Calhoun, Carroll, Chickasaw, Choctaw, Claiborne, Clarke, Clay, Coahoma, Copiah, Covington, DeSoto, Forrest, Franklin, George, Greene, Grenada, Hancock, Harrison, Humphreys, Itawamba, Jackson, Jasper, Jefferson, Jefferson Davis, Jones, Kemper, Lafayette, Lamar, Lauderdale, Lawrence, Leake, Lee, Leflore, Lincoln, Lowndes, Madison, Marion, Marshall, Monroe, Montgomery, Neshoba, Newton, Noxubee, Oktibbeha, Panola, Pearl River, Perry, Pike, Pontotoc, Prentiss, Quitman, Rankin, Scott, Sharkey, Simpson, Smith, Stone, Tallahatchie, Tate, Tippah, Tishomingo, Union, Walthall, Warren, Washington, Wayne, Webster, Wilkinson, Winston, Yalobusha, Yazoo

New Mexico - Bernalillo, Chaves, Cibola, Colfax, Curry, De Baca, Doña Ana, Eddy, Grant, Harding, Hidalgo, Lea, Lincoln, Los Alamos, Luna, McKinley, Otero, Rio Arriba, Roosevelt, San Juan, Sandoval, Santa Fe, Sierra, Socorro, Taos, Torrance, Valencia

Pennsylvania (1 newly added) -  Adams, Allegheny, Beaver, Bedford, Berks, Blair, Bradford, Bucks, Butler, Cambria, Carbon, Centre, Chester, Clearfield, Clinton, Columbia, Crawford, Cumberland, Dauphin, Delaware, Erie, Forest, Franklin, Fulton, Greene, Huntingdon, Juniata, Lackawanna, Lancaster, Lawrence, Lebanon, Lehigh, Luzerne, Lycoming, McKean, Mercer, Mifflin, Monroe, Montgomery, Montour, Northampton, Northumberland, Perry, Philadelphia, Pike, Potter, Schuylkill, Snyder, Sullivan, Susquehanna, Tioga, Union, Warren, Washington, Wayne, Westmoreland, Wyoming, York

Rhode Island - Bristol, Kent, Newport, Providence, Washington

Wisconsin - Milwaukee

In the current pipeline for updating in April 2020
--------------------------------------------------
Connecticut - Statewide
Kansas - Statewide
Montana - Statewide
Tennessee - Davidson County
Wyoming - Statewide

In the pipeline for updating in May
--------------------------------------------------
Texas - Statewide

Based on feedback and county challenges, pipeline planning is always subject to change. As always, please contact us if you have any questions about accessing or using the data, if you find issues with any of our data, or you have any comments or questions about our data in specific areas or states. We also love to hear from you about which counties or regions you’d like to see us update next, as it helps inform our planning process.


Thank you for being a part of Loveland!

Happy Mapping!

Re-Post - Nationwide Parcel Data: From Cold, Metal Chains, to the Spatial Foundation of American Society, to your Database.

By Sahana Murthy on March 26, 2020 · Democratizing Data

Reposted from Jerry Paffendorf's guest blog on makepath.com 

https://makepath.com/parcel-data-landgrid/

----------------------------------------------------

Note about the author of this guest post: Jerry Paffendorf is co-founder and CEO of Loveland Technologies, makers of landgrid.com. Landgrid and makepath partner together on special projects.

“In the mid-19th century, when the cold tongue of land that is the Michigan peninsula was first being sliced up for development, the surveyors began to discover problems with their measurements, particularly during the winter. The lengths of metal chain they doggedly carried and laid out like giant rulers across the forests and swamps would shrink when the temperature dropped below zero.

“The resulting inconsistencies would only add up to a few inches a day, but over the vast distances of midwestern America the shrinking chains threatened to cause future disputes between landowners. Until a conscientious surveyor called William Burt came up with a solution: every frosty morning, he built a fire and warmed up his chain until it expanded back to exactly its original length.

“Such diligence, respect for figures, and slightly bloody-minded defiance of the elements is a very American combination. So to try to understand the country by describing how it was first surveyed and divided up, as this book does, is likely to be a fruitful enterprise.”

From a book review of Andro Linklater’s book, Measuring America, published by The Guardian

My company specializes in providing nationwide parcel data: the legal boundaries of properties along with addresses and information like ownership, land use, occupancy, buildings, and other data points that can be attached to parcels. It’s been a consuming pursuit as property boundaries underlie everything and form a natural ice cube tray for other data.

Parcels look like this, seen here with building footprints overlaid:

We are based in Detroit, Michigan and originally got into parcel data to help address challenges in the city. At its peak population in 1950, Detroit had about 2 million people and was America’s fourth largest city. Today it has about one-third of that, making its ratio of people to properties much different than the mid-20th century, with all of the attendant stresses to the tax base, maintenance, services, and occupancy you can imagine.

During Detroit’s bankruptcy in 2013 we were hired to assess the current land use, occupancy, and conditions of every single parcel of land in the city for a project called Motor City Mapping. The interactive map, photos, and dataset are archived at motorcitymapping.org.

For that project, 200 Detroiters used our software and mobile app to photograph and describe each property. In the process they identified more than 50,000 vacant buildings and tens of thousands of occupied homes that were at imminent risk of tax foreclosure, among other pressing challenges and opportunities in the landscape.

The data was combined with other datasets and kicked off a wave of innovative data and mapping projects in the city, and provided some much needed insight into the landgrid.

We wanted to be able to do something like that anywhere in the country, which meant we needed nationwide parcel data. Not having any other way to attain it, we set about collecting it from every single county ourselves.

That really sent us down the rabbit hole and got me reading about the history of how and why these parcels came to be in the first place. If you’re looking to read some fascinating history that you may not know much about — I certainly didn’t — do yourself a favor and google the US Public Land Survey or pick up Andro Linklater’s book, Measuring America: How the United States Was Shaped By the Greatest Land Sale in History.

(Image Credit - https://www.sfei.org/it/gis/map-interpretation/projections-and-survey-systems#sthash.96FRkw4y.dpbs)

Long story short, at Thomas Jefferson’s urging, and to create a spatial framework for a new nation of citizen farmers, starting in 1790 most of America outside the south and the original colonies was measured out and subdivided into square mile sections by people dragging metal chains through the woods, across rivers, over mountains, you name it. Every six by six squares was called a township. Townships snapped into counties, and counties snapped into states. The land was typically auctioned and then further subdivided over the years, decades, and centuries into the residential, commercial, industrial, agricultural, recreational, and wild parcels we know today.

You can stare at maps and see the straight lines of many states and counties, but it’s easy to overlook that they represent a nested fractal leading down parcels, which are the atomic unit of owned and managed space in society. Within that landgrid are so many accidents and arbitrary happenings that it makes you wonder how we might one day redraw it or return parts of it to nature. (PS if you like pictures like the one from Wikipedia above, check out the Instagram account, thejeffersongrid, which focuses on big square parcels.)

Our dataset currently consists of 144+ million parcels covering 95% of US residents. You can see a coverage map here, and you can see details about the data by clicking through to any county. Sometimes when the work is hard I think about that person warming their chain in a fire before dragging it through the woods another day, and it feels a little less hard to wrangle digital files with a LaCroix next to my keyboard.

We make the parcel data available for other people to use in their own research, apps, projects, and databases. Every day I wake up with a kaleidoscope of customers and partners and curious people in my inbox who span real estate, energy, insurance, agriculture, forestry, marketing, transportation, outdoor recreation, government, planning, and other industries that touch property, land, housing, and spatial analysis.

Sometimes people want to use the data for geocoding other datasets to a map. Sometimes they need to know who owns things. Sometimes they need to tell open land apart from land with buildings, or they need to identify occupied or vacant properties. Sometimes they need to do door-to-door outreach. Sometimes they use the data for business and sometimes for the joy of discovery.

Moving into the future, we’re really excited about the opportunities for combining parcel data with Machine Learning and aerial imagery. With the parcel boundaries as the picture frames, there are many new data fields and insights that will come from training software to identify the features within a parcel and turn that into structured data to give even greater insight into the grid and how we inhabit it. 

All of this is what makes the work we do so exciting, and it’s why we value partnerships with data scientists like makepath who can take a massive, fundamental dataset like this and make new knowledge from it. 

If we can assist you with parcel data, or if you just want to rap about how crazy the history is and what the future of parcels could look like, please reach out to me at jerry@landgrid.com. And please be safe in these unprecedented times!