 Good afternoon, everyone. Welcome. My name is Yulia Panfill and I'm the Director of the Future of Land and Housing Program at New America. We're so pleased to welcome you today to the launch of the foreclosure and eviction analysis tool web application that is being jointly released by New America and our partners DataKind. Today we'll walk you through the application. We'll tell you a little bit about our journey of how we got here. And we'll also hear from two special guests from the eviction lab and also from our partners at Indiana University Purdue about how they are also using and collecting eviction data and foreclosure data to bring more clarity to how and where people are losing their homes around the country all towards an effort to keep more people housed. Next slide, please, Alex. So, as I mentioned, we will start with a demo from my colleague Sabiha Zainalbhai and Caitlin Augustin, who is the Vice President of Programs and Product at New America. Followed by that, we will hear from Matt Dietrich, who is a senior research data analyst at the Polish Center at Indiana University Purdue. IUPUI has partnered with us to use the feat tool to stand up what is the country's first ever statewide dashboard of evictions and mortgage foreclosures. And we'll hear from Juan Pablo Garnum, audience and community engagement editor at the eviction lab about the eviction labs eviction tracking system, which is pulling in eviction data from 10 states and 34 cities and counting. And we'll close with a little bit of a discussion around why this data collection and use is so critical towards the ultimate goal of keeping more people housed. Next slide, please. So I'll start with the challenge that kind of got all of us started down this path. Several years ago, New America began really identified a major challenge in the housing space, which is that despite the fact that millions of people lose their homes each year through evictions and mortgage foreclosures, very few states and cities have reliable data on evictions and foreclosures. They don't know in any given year how many people are losing their homes, let alone things like which neighborhoods are hardest hit, which populations are most at risk for housing loss and whether evictions and foreclosures are rising and falling at any given time. And without access to this data, it's really difficult to develop targeted policies that, for example, distribute rental assistance to communities most at need, stand up legal aid clinics, pass policies that help stabilize these vulnerable populations and keep them housed. Next slide, please. And so at the beginning of the pandemic, really, we, with our partners at DataKind, began manually collecting court eviction and foreclosure data from a handful of jurisdictions. And from in those jurisdictions, we were able to take that data of individual evictions and foreclosures and pull it into different analyses that show, that show down to the neighborhood level, eviction and foreclosure hotspots, for example, and demographic correlation analysis to show who was most at risk for evictions and so on. And we only did this in a few places. What you're looking at right now, it appears that the slides are a bit blurry. Hopefully we can work on the back end to make those slides a little bit less blurry. But you're looking at a heat map of Maricopa County, so the Phoenix metro area that showed from 2014 to 2018 the prevalence of evictions and mortgage foreclosures across that metro area. And as a specific example, once we had this data, we presented it to the city of Tempe, the city council, and based on these heat maps as well as demographic analysis, what the city council was able to conclude was that neighborhoods with high immigrant and majority Spanish speaking populations were at disproportionate risk for housing loss. And right there at that city council meeting, they voted to stand up a Spanish language legal aid clinic to assist homeowners and renters who were facing evictions and foreclosures. So these were the types of very concrete insights and actions that could be taken when jurisdictions had access to this data. But of course, as a Washington based think tank, we quickly concluded that it's not really our role to go around the country collecting this data across really thousands of jurisdictions. And so we undertook a process that has span a few years now to build a tool that would allow local leaders to do this themselves. Next slide please. So starting in 2021, New America and data kind partnered with 14 different cities and counties to develop the prototype a, you know, get hub based version of the foreclosure and eviction analysis tool, which Caitlin and Sabiha will voice over in a second this tool, more or less performed a similar analysis to what we were able to do through our research, but it allowed local leaders to upload their own eviction foreclosure data, and do this analysis themselves. That tool was released in 2021. And while it was incredibly useful, we heard a lot of feedback that it was challenging for folks who didn't know how to code for example, to interact with a command line interface version. And so over the last two years through generous support from Schmidt Futures and also the Rockefeller Foundation, we've been working to develop a web interface that really automates these processes and makes them much simpler. And with that, let me stop and hand things over to Caitlin and to Sabiha who can tell us a little bit more about this new tool, the web interface and take us through a demo. Sure. Thank you so much, Julia, for the tour through history there. It's been quite a journey. Thank you to everyone who's attending this conversation today. We're really thrilled to have you here and to be a part of a conversation about how we can use tools and data to improve housing nationwide. My name is Caitlin Augustine. I'm the Vice President of Product and Programs at DataKind. For those of you who might not know DataKind, we're a global nonprofit organization focused on tackling the world's toughest challenges with data science and AI. And one of the ways that we meet this mission is through the creation of software products in the public interest. And we build that software through collaborations with social sector leaders across domains such as New America and the 14 partner sites that New America brought into this conversation to help shape feet. We also couldn't do this work without philanthropic support, so thanks to the many supporters who invest in the technology for public good. Julia gave you the background of what Feet's Genesis was. Two years of analysis driven collaboration that led to the publication of the Displaced in America and Displaced in the Sunbelt reports. And then that movement from the individual location analysis to the creation of an accessible self-service tool that first came out in 2021. That initial self-service tool, it was useful, but it also required a level of knowledge around Python, around GitHub, around running things in the command line. And you can see a quick screenshot here of what the outputs looked like in the tool that was run in the GitHub repository. And it really prompted us to say, this is useful. We were able to see great strides through groups like the evictions and foreclosures group of Central Florida who were able to take the knowledge that out of Feet and start to do interventions in their community. But we saw a limitation. How could other entities pick this up? And so moving from that code base to the web application that you can experience today, necessitated the building of a totally new product. Well, the outputs and calculations are similar to what you experienced if you use the previous tool. The infrastructure and the user engagement approach has completely changed. While that coding approach was designed to work for some, this is designed to work for all. And before Sylvia actually takes you through showing the multiple pages here of the Feet application, I just wanted to give a quick, how did we get there moment or how did we get here moment. The first thing is it was not data kind and New America working alone. You're going to hear from two collaborators also on this call, but it was dozens of individuals and organizations who participated in user feedback sessions. There was a packed standing room only room at the NICAR conference in February of 2023 with eager journalists running the first version of the front end. And getting all of those different touch points and those places of feedback helped us determine how a user might interact with the tool and what was most important. And so one of the things that came out very quickly is that data accessibility is still a significant challenge for actors who want to understand housing loss in their communities. This is a huge body of work that New America has been leading a lot of research on. I encourage you to look at the reports from their website. One way that we figured out how to at least start to solve this within the Feet web application was to incorporate eviction lab data. And we were able to do that thanks to the great collaboration and collaborators with the eviction lab team at Princeton, and you'll hear from Juan Pablo later. We were able to identify new features. We had a long list of desired changes that users hoped for. But the thing that kept rising to the top of every list was visualizations, visuals. Can I interact with a dashboard? And so we were able to build that in to this tool. We're also able to improve the user experience by including wayfaring, giving markers of how long have you progressed through your analysis. How much effort has been put in? What's gone wrong if you do get an error? Things that didn't exist when we were running this purely as a code base. And finally, another bit of changes we made were increasing user security. You can incorporate authentication, data privacy, not requiring an end user to download this code base to use the tool, for example. And then finally, in order to make sure that this tool was successful, we learned across free and open source technology builders and adopters. So working with entities across the Schmidt Futures product studio who have launched tools or were building tools as well. Coordinating with the US Census working group members to help build the most stable tooling on the US Census APIs. And then really engaging with new and different user communities across the journey. And with our ultimate goal of having a tool accessible to all housing actors, governments, policymakers, journalists, community advocates and more, we're just so thrilled at the results and look forward to sharing this with you. So I'll hand it over to Sabiha. Yeah, thanks so much, Caitlin. Yeah, so my name is Sabiha Zainalwai and I am the Deputy Director of Domestic Housing on the Future of Land Housing Program at New America. And we're really excited to demo this tool for you. So I will just jump in. I will start by sharing my screen. And what you are looking at right now is actually on New America where, you know, the foreclosure and eviction analysis tool has a homepage on New America's website that houses not just a link to the tool, which has its own URL, which you'll see in a second, but also all of the surrounding resources that you might need in order to access more information on the tool, read about different use cases, a user guide that I'll go to in a second that goes, that talks through data access, data interpretation, and the exact steps that FEET uses to transform the data. But in order to access the tool, which is at www.feetapp.org, this is, you know, what the homepage of the tool looks like. And, you know, Caitlin mentioned data security and so in order to use FEET, you do have to create a login and a password. One of the benefits of this is that with that login and that password, all of your previous FEET runs on your data that is uploaded. It's actually stored, not the raw data, but the actual analysis is stored to your username and so you can access it at a later date. But I just want to walk you through really quickly what the homepage contains. There are two points of entry for a user to be able to benefit from this tool. And so the first one is to upload your own data. And that would be, you know, whether it's eviction filing data, eviction judgment data, or mortgage foreclosure data, or all of the above. And if you follow some data formatting guidelines and if your data contains some specific fields, you would be able to just seamlessly upload your data and access FEET analysis. And I just wanted to share a little bit, you know, if you are uploading your data, so let's say that's access through public court records or from the State Supreme Court, for example, if it's data that you've accessed, you know, through a local housing intermediary, or another source altogether, the main pieces of information that you will need is that your data would have to be recorded at the individual eviction or foreclosure record level, so, you know, not in the aggregate. And then you would also need a geographic location. So that's either a property address or because all the FEET analysis is at the census tract level, a census tract or G-O-I-D, and you would need a date. You know, a date of, for example, eviction filing or a date of the eviction judgment. But your data can cover any time period post-2016. So we actually have a data template here. It doesn't need to be included in this, but just to show you what the required fields are. Location, date, city, state, zip code. And then here at the bottom you can access, these links will take you back to that FEET homepage on our site. And I just want to share the user guide. It's a very in-depth, if you are attempting to use the tool, it's a very in-depth step-by-step guide that talks about all of the data and the formatting specifications that are needed, as well as all of the processes that FEET takes to transform that data, the geocoding and all of that. And then lastly, the interpretation of all of the outputs of FEET as well. So going back to the homepage. So I just want to show what it would look like if you were to upload your own data and then you can walk through the analysis that FEET outputs. Uploading data, it would require a CSV file. And so I'll just start to run. I have Bronx data, Bronx eviction judgment data from the New York City court system, and this data is already formatted according to FEET specifications. You will be able to upload it. FEET will tell you if your data is adheres to those specifications. And if not, it'll tell you what specific field is either missing or, you know, will require more attention. And then you can run FEET. And I will mention here that the time that FEET takes to run on your data will depend on the size of your data file. Obviously, a larger file will take a little bit more time. But that's why we have this progress bar here that shows you exactly where in the process FEET is of transforming your data. But in, you know, in the, in, so we don't have to sit here and watch FEET geocode, you know, 68 sets of data. I'm going to actually shortcut this process by taking you to a previous run that I did this, this morning, just this morning, where you can access both a zip file of the full set of FEET results. So what that includes is it, you know, all of the underlying data files that are used in the analysis as well as the results of the processes. So for example, there's an output file that shows the results of census geocoding and which data geocoded and which did not. So a lot of transparency within that file itself. And it'll, there's a file that shows, you know, where evictions and foreclosure data. It will show it aggregated at the census tract and then as well 75 American Community Survey variables that will be appended to those census tracks to conduct further analysis. It'll in this zip file, I'll just show it to you. There will also be the output of correlation analysis, which we can talk through in a second, then also all of the underlying data to create time series as well. So basically just data organized by month and by year. So essentially what FEET is doing is it's taking raw data and transforming it into underlying spreadsheets as well as data visualizations that can be useful in both your own analyses and then also for creating kind of ready made analyses as well. So if people have questions about that, I'm happy to go more into more detail later later on about what exactly is in all of those zip files. And the user guide that I showed also walks through each output in the zip file and shares exactly what's in it and how it can be interpreted. But I wanted to show also the other way in which outputs are produced. Caitlin had mentioned new features and this is something that was responsive to a lot of the users that we had talked to who might access data, but not necessarily have time to go through all of that data, clean it, standardize it, but what they are actually looking for is month by month as new data comes in the ability to seamlessly visualize it. And so the vision so in addition to the zip file there for a select set of visualizations, there will be a dashboard of FEET results directly within the web application itself. And so like I said, this is Bronx data that is from, that is from for eviction judgments that was from 2019 to 2021. And so you can select the year that you would like to, if you would like to see it across all of these years, which is I think what we'll go through. You can see the data at a glance. So just a couple of top line statistics pulled from aggregated and pulled from that data. And then FEET creates heat maps in which you can look at, you can, you know, this is a census tract heat map. You can see here it's covering the geographic location of the input data, which is the Bronx. And, you know, the color of the tract signifies the essentially either in this case the judgment rate but it could be the filing rate or the foreclosure rate. And you can also see how that you can see some underlying data that's really useful here as well. So this tract, for example, that I'm looking for looking at has a 0.8% eviction judgment rate across those three years. From 2019 to 2021. And then also you can see that this is 1.52 times the average of the eviction judgment rate across all of the data. So all of the Bronx data that we input. And then you can just see the number of judgments total versus the number of venture households, and then a couple of some demographic and underlying demographic and socioeconomic information as well. And then in terms of the correlation analysis, so correlation analysis that we've looked at in the past has been essentially a proxy for understanding who is most at risk as most people here probably know eviction records don't have eviction and foreclosure records that usually don't have any demographic information attached to them. And so, you know, runs a statistical analysis on those 75 American community survey variables to kind of measure the strength of the relationship between in this case eviction judgments and all of those 75 variables. So, you know, race and ethnicity about housing variables, a bunch of socioeconomic variables as well educational attainment, and the results essentially show. So actually for this there were no statistically significant results for this specific run. If, for example, what feet would show here would be, you know, it would show the strength of the correlation and if it was statistically significant. And so an interpretation here would be across the census tracks in my data. So, you know, household census tracks that had X characteristic are more likely to or tend to experience higher rates of eviction judgments than other tracks in the data. You know, this data, I think it's meant to again be a proxy for who is most at risk of eviction. But you know it's obviously should not be interpreted as causal and it also a lack of a finding here doesn't necessarily mean that there is no relationship between those two is just based on that data. There was no, nothing significant was found. And then lastly here, there's a time series where you can look at how data across the years over time, the total count of eviction judgments. And so, like I said, this is kind of a snap to a visual snapshot of what is in the zip file which is much more inclusive. And so I'm happy to talk more about this when we get to the Q&A or answer any questions that have come up but essentially feet is a user driven tool. And so it will transform the data that is uploaded the eviction filing eviction judgment mortgage foreclosure that a user has access to and can upload. But, you know, with all as with all housing data. It's important to have a fairly good understanding of what the data is that you're uploading, how to interpret those metrics, or, you know, how to interpret the type of data, and then carry over that interpretation into the outputs that feet is providing. And then one last thing I wanted to share was just I'm going to pass it to Juan Pablo in a second who will share more background on eviction lab and eviction tracking system but a new another new feature of feet is that all of that analysis that I just went through the zip file and the visual dashboard users can access that straight on eviction filing data for 10 different cities, and I think 34 and counting cities sorry 10 states and 34 and counting cities, and this data is uploaded on a monthly basis as eviction lab uploads their own data. And so, again, just kind of broadening access since since access to this data is one of probably the biggest barrier to using a tool like this, figuring out ways where people who might not otherwise have access to data to use a tool like this can can still benefit from, you know, of seeing housing loss analysis in their community. So I'm going to. I don't I don't know if any specific questions on feet came in that we want to agree. Yeah, go ahead. Thanks so much to be a we had one question, which you touched on briefly just now actually but if you'd like to add a bit more. We had a question from the audience about where or how one would go about sourcing the data that would input into feet. That's a great question. So, I think first I would look if you're interested in eviction filing evictions. I would first look I think to see if eviction labs eviction tracking system covers your jurisdiction or your state. But other, but, you know, even if that's the case it's there's other there's other metrics you might be interested in like eviction judgments are executed evictions and mortgage foreclosures as well and so we actually have a lot of different places where jurisdictions typically source that information. A lot of it is straight from the court system itself. So, you know, through records, court records. Some of sometimes that's publicly available online and other times, I think Matt will share in a little bit. It can be available through, you know, as a state based entity like the state Supreme Court. Often times people are scraping that from websites and creating access to it. You can check with your county or city government. Oftentimes there are community based organizations who are using that type of data for outreach. And so there might be entities within your jurisdiction that already have access to that data are already attempting to create a pipeline of access to that data. Thanks, and I'll just add quickly that if you are in that situation. Please reach out to us. New America has over the years accumulated some. Yeah, so I'll just finish really a sentence right there if other people can hear me but we have helped quite a few jurisdictions gain access to their addiction foreclosure data and kind of make those connections and figure out what the route is to so if anyone is in that position, please do reach out to us. With that I'm going to pass it over to Juan Pablo but have I'm sure more questions have come in and we'll we'll definitely take them at the end so please keep sharing your questions. Thank you everyone thank you so yeah and the the first thing that I have to say thank you so much to everyone in New America and data kind that have worked so hard in creating this project. And to briefly talk about what's what's out there kind of like go a few steps back and explain what do we have from in terms of data because I think we still find that a lot of people think that addiction data is easily available everywhere. And that's not the case. We've been working for years to try to get addiction data. And it's still very difficult in some particular states and cities. But luckily, addiction lab has been able to develop two databases that I'm going to talk a little bit about. I'm going to share my screen now. Let's see if this works. Yeah. The first database that we created is a national eviction map, which I'm not going to go super deep into it but you can access it here. And it has data that it's historical data from 2000 to 2018 of every state and every county in the 50 states, including eviction filings and eviction judgments. So that's that's one of our databases that we have for you available. And during the pandemic, the urgency that you know this this health emergency implied like pushed us to try to create something that would be more in real time, which obviously it's been always like the real thing to have. But as I was telling you, having this data readily available is not always super easy. So the vision lab team went out and tried to seek which places we could get this data in a reliable way month to month and in a way that that it would be, you know, accurate. So that's when very early on in the pandemic we created the eviction tracking system which you can access either in our menu, sorry. Or you can just go here and see up to date maps and here you will find data for 10 states and 34 cities that we track. We're trying constantly to add more and we love to collaborate with people in states and cities to do that. It's it's it's not an easy process but we're very happy to receive your emails about it. You can email us at research at eviction lab.org and you can use that same email for anything that you if you have any questions or anything. So in the ETS or eviction tracking system you're going to find filings for like a summary of filings for every city. A lot of comparisons with the historical averages which we get from the previous database that we told you and that way we can know like if in this post pandemic scenario we are like at the pre pandemic levels or beyond that. And obviously we have more detail for each of these locations which you can see here and for example I'm just going to go to one example Houston which we have several details that vary depending on the city in the case of Houston for example we we can go in very good detail we have like total amount of filings we have filing rates also in some situations the serial filing rates that they speak about you know many people get filed against several times a year. And that's like a practice that some landlords you know often do to collect rent. And here there's like an important detail that we can see like month by month since the start of the pandemic. And data for filings you can always download the CSV here which is the CSV that actually is preloaded in the fit tool. And you can also see other details like for example who are the top evictors so we can see here that all the eviction filings they are 70% of them in Houston are concentrated in just 100 buildings. You can also download all this data in some cities we have like average claim account amounts. You can see the location of these filings which is something that also feed allows you to now. And you can see some data related to demographics which for example in this case is like evictions by majority of race ethnicity in a neighborhood. And also we have imputation so these are estimations of filings and you can see them here compared to the percentage of renters of each of one of these demographic groups. So that's it. We're very happy to receive any questions and then you can always email us at research at evictionlab.org. And I'm also happy to answer questions afterwards. So now I'm going to let Matt speak about this excellent work that they've been doing in Indiana. Thanks so much. I really appreciate that and I'm going to try to share my screen now and just going to give a real quick. Overview of kind of what we've been working on in Indiana and Indianapolis at the Polis Center in partnership with New America and others throughout the state so I'm a senior research data analyst at the Polis Center which is located at soon to be Indiana University Indianapolis. And we've been a lot of our work is focused on kind of community informatics and empowering communities with data and information. And the housing space is one that we've been really passionate about for several years now and the opportunity came along for for us to try to develop this this more accessible interactive online dashboard that was across the whole state of Indiana and really kind of focused honed in on like the interests of our Indiana stakeholders are different partners throughout the state. We've been talking to you over the last several years and that New America has been working with. And I'm just going to kind of quickly overview some of the process that went behind this endeavor and then kind of give a quick preview of our dashboard that just launched. I believe five days ago it was December 7th so fairly recently on that we're really excited about and excited to share. So a lot of what what NC designing our dashboard was it was kind of two full we first we looked through a lot of the great kind of templates and that are already out there such as fiction lab which is kind of a gold standard for amazing eviction data visualizations and data accessibility and wanted to see how can we build off of what's already been done and but meet some of those more specific interests and, you know, kind of data desires of those that are in our community in Indiana so we met with, you know, dozens of stakeholders representing lots of different groups throughout Indiana ranging from, you know, the university to, you know, the legal system we know judges nonprofits met with those from you know the public media as well to try to get a better understanding of okay what's what's missing that you that you would like to see more of and how can we work towards that. So we convene a lot of these meetings and that gave us kind of a great framework to what to strive for but that was kind of honestly that the fun part the hardest part I'm just to cut right to the chase of this whole process wasn't you know using the really easy and seamless and great meet the stakeholders were great and give us amazing feedback. The hardest part was actually getting the data and trying to decipher and and kind of break down the data in a way that's usable and so that we could actually put it into the fee tool and you know kind of clean it and documented for kind of sustainable and future use so we actually did a data request. I think it was back in April to the Indiana State Supreme Court and you know they were there helpful we had a lot of communication back and forth but it took time by the time we got the data it was I believe early August so it took several months and after we got the data. There was a lot of stuff on the back end that we had to do in order to kind of clean it and get it to a point where we could actually input it either into the tool or just organize it more seamlessly for our own databases so for example there is over 3 million rows of data that we received from 2020 to 2023 this include eviction and foreclosure cases and within each case there was sometimes dozens of of court actions that were also recorded in the data so it's trying to go through all this this information and pick out okay was this an eviction filing wasn't an eviction judgment and was it a foreclosure there is ev classifications for evictions and MF for mortgages which was a great starting point but something that we really wanted to do to set us apart from stuff that's out there currently is differentiate between eviction filings eviction filings and eviction judgments and that's where it took a lot of time to do and I'm going to forget I'm going to kind of try to breeze through this I can get to the dashboard but we use SQL to kind of do all that filtering and organizing we actually met with judges to kind of talk through okay what are different what are key words are associated with which specific case event types and orders. That helped us better differentiate that will between what was a filing what was an actual judgment. And we're able to kind of come up with these classifications so that we could actually format it as to be how it's showing in the proper way to input import into the feed tool so that we can get some really cool analysis that you know help that and I'll show these in our dashboard a second to differentiate important demographic characteristics of these hotspots and addictions and foreclosures and whatnot, but there were a lot of limitations and I'm happy to answer some of this in the q&a but We have what we've done in the past and what you know what Pablo was showing with addiction lab was kind of differentiating what are the worst victors right who are these worst properties and whatnot and we've done that in the past in Indianapolis but where we actually kind of aggregated a lot all these different LLCs to kind of find out who the single soul owner was. We had aspirations for potentially doing that the state level it just got a little bit too complicated and we didn't quite have the funding and time to do it at a state level because a lot of LLCs are kind of you know shelter under various owner names and we didn't really want to point out the kind of single property just because that you know with limitations with our dashboard it didn't really fit in as seamlessly as we would hope, but that's something that we would like to do forward, potentially with more funding and time. It's something really important to point out with our data is that when we obtained it from the Supreme Court and this is something for I think folks to keep in mind. Regardless of what state or city is kind of the limitations of whatever your data source is and the data new database that the Indiana Supreme Court is using based on the small claims court classifications and whatnot we didn't have evictions data for 2020 just 2021 to 2023 even though we requested 2020 eviction data we do have foreclosure data from 2020 but not evictions even though there was not many evictions in 2020 but just important to always keep in mind what are some like limitations with depending on where you're requesting from and something else to keep in mind is that with the different courts file information differently. So the legal language and the nuance of differentiating between filings and judgments can get complex depending on you know how large of a geographic scope you're really trying to hone in on judgments versus filings and foreclosure so that's Now I'm going to kind of go over to the actual dashboard so what we developed and what we're really excited about but before I do that I just want to point out this is a team effort that takes a lot of individuals ranging from those that do the actual data cleaning and processing to those that are putting together the dashboard to those that are engaging with community stakeholders and organizations so including the stakeholders themselves who provided feedback so there's a lot of input that went into this but without further ado I'm going to kind of hop over that and just in a few minutes kind of just preview what it looks like and I believe we'll be sharing a link to this so that hopefully you all can access so I'm switching my screen sharing now so you should hopefully be able to see the dashboard so this it's we did this all in story map collection stuff a lot of different ArcGIS products went into this but essentially we come to this home landing page where we essentially kind of point out how to navigate and go through the site so it basically describes the project it has a link to our home website at Savvy.org where we have even more housing information and articles and stuff on a lot of different community topics that are interrelated with evictions and foreclosures but the cool thing is we try to give a little snippet of each page so you understand which tab each tab above is about you can get to each page either by clicking on the buttons here or just navigating up top we have methodology report you can click on some FAQs so a lot of information there that is hopefully helpful to showcase how we actually came about you know processing all the data so the first tab is looking at specifically just evictions and foreclosures kind of at the surface level we have some different maps that kind of showcase at a census track level you know where these occurring what are the rates what are the counts we also as I'm scrolling below and sorry if it's loading slowly but we break it down by evictions and foreclosures we have a time series where you can actually go through and look at the maps and kind of click through month by month and see how things have changed over time at both census tracks and township geographies and again see monthly how things have changed over time the last several years and you could also kind of see by county how you know there's been trends over time as well and then the next page is this evictions deep dive where we really just dive in to a lot more of the details surrounding eviction data so both filings and judgments we actually hold in data from eviction lab here where you could actually kind of quickly and easily toggle between different states and see how does your state compare to others you can maybe just show one or two at a time if you want you could also do this by city again this is all data available in eviction lab we provide a link below where you can search for you know different cities and see time series trends over time and this is where we incorporate the feed tool so the amazing thing about the feed tool is that once we input all of our address level eviction data and foreclosure data you can get these outputs like Sabia was talking about as to what variables are correlated both significantly and significantly with evictions or and or foreclosures in a given census track and how does that apply across say the state level which is what we did here trying to see across Indiana what variables are associated with eviction filings and judgments throughout Indiana to help give more insight as to what may be important drivers or you know covariance with evictions we also dive in some more we did some data processing to see all right how many sealed eviction cases have happened over time based on geography based because of new rulings in Indiana we did case length how long per month our cases is it from filing to hearing and some other resources like rental assistance priority index I only have another like minute so I'm going to quickly to say that we do demographic data as well we use outputs from the feed tool to kind of toggle between you can look at click on different areas of the map and see all right how what's the average rent in this given census track poverty demographic breakdown etc. And lastly we try to incorporate qualitative data where individuals can share their stories with us through a form and we can put it on this website to kind of put a name behind the data and lastly we incorporate a lot of different resources here so that individuals can kind of click and explore helpful things for tenants and other data sources including eviction lab the feed tool and some other deep dives on evictions we've done with that. Happy to take questions now I'll pass it pass I'll stop sharing and pass it back to the new America. Yeah, thank you so much Matt. You made it look really seamless but so much work went into putting that dashboard together from, you know, the initial stages of data collection to gathering input to actually creating the dashboard so. I there are a couple of questions that came in during the presentations and I just wanted to make sure we get to those. And so, Caitlin maybe this maybe you can take this one on feed. Someone asked, are we able to see the results or analysis of the data uploaded from others or can we only see the results of our own data. Yeah, I can definitely answer that so when you are you have an individual user ID and a user login so you are able to only view what you have logged in to see. However, there are options to download and then share your output so you can download your the zip file that to be shared and that can be shared to others. You could download images out of the each of the charts and that visualization dashboard, but you can't directly share the previous run to others. Yeah, thank you. And one problem someone asked is there a way to overlay like this is also for feed is there a way to overlay feet maps on other existing maps to see connections between this and other geospatial data. I can actually take a quick stab at that and then see if you have anything to add. So I had talked a lot about the zip file. That contains a lot of the underlying data that what goes into creating visualizations. So right now the visual the heat map that I showed is what he currently generates we're always gathering feedback and curious in users thoughts on what would be additive and beneficial. But to create that heat map. There is a underlying Excel spreadsheet that has all of the data geocoded with the American Community Survey. I can socioeconomic and demographic information appended and all of kind of the eviction rates or eviction filing rates fiction, you know, eviction indices all of the calculations that feet does in one spreadsheet and so a user could use that to create their own visualizations and append their own data. Census track level data onto that. So, yeah, we're trying to cater to an array of users those who want to take the data and do more with it and then those who want to sort of visualize the data that the data that they have in hand. Anything to add to that. No, I think I think you explained it very well. Our data you can all download it. Obviously, we try to work a lot in terms of user experience and the more the more elements more tools that you add to any kind of visualization of data. The there's like, it's pretty easy that that experience might be worse, you know. Obviously, we're happy to help people. One thing that it's important to say is that depending on where you are in the country, you might have limited access to, you know, to the geographic aspect of the data, right. So for example, in New York City, we're able to have zip code data only, which it's not ideal in other places. We are able to go down to census tracts and in some places we don't even have specific specific data beyond the city, for example. So, yeah, we were where people can download the data, they can download the CSV file and play with it. And if they have any questions about it or any need any advice about how to do that and how to add other elements, other variables to a visualization, we're happy to help them or collaborate with them. And again, the email I've said it before, it's research at evictionlab.org and people also can reach us in any of our social media platforms. We're everywhere, but in TikTok, right. Yeah, thank you, Juan Pablo. So I wanted to ask kind of a general question of everyone. You know, so based on discussions that everyone here has had with local housing communities and developing feet or standing up a dashboard, I'm curious, you know, what else do people wish that they that feet could do or that what else have you heard that people need assistance with in order to be able to use it. So maybe we can just go around and then I can, I might weigh in last. I can start. I guess something that kept that folks kept bringing up and talking with us and there's actually efforts underway in Indiana right now is trying to dive in a little bit more into the eviction like experience and get data, quantitative data from that. What I mean by that is like in the courtroom, for example, like how long are people's, you know, what's the average hearing time per court and then displaying that and getting that data. And so we have, we've been talking with folks stakeholders where they're working with court watchers to try to compile that data like how long is each person getting heard in court, how many have legal representation. And how can we pair that kind of data along with like the actual, you know, what the eviction and, you know, eviction judgment like data to give a more holistic picture to help different organizations and advocates like address, you know, the more, I guess, yeah, holistically the housing problems. That's something that we've heard and people are working on and then hopefully we can maybe incorporate that into some kind of data display as well eventually. Yeah, I'll echo that. I live in Orange County, Florida, where in Florida is one of the statewide scaling partners of feet. And one of the things that we've heard in our conversations here is how can we situate a tool like feet in the continuum of care. So a lot of the housing actors are working with a homeless population and trying to get that homeless population shelter. They're also trying to get enrollment into school programs and other pieces of benefits. And so where does the feet tool and sit in that continuum and how can the information like integrate across so is there a feet for benefits where you could start to see where not just say housing losses but where other benefit services might be more applied or can the information be handed off to say you need to run a community event in this area because we see a co-occurrence of housing loss and that seems to be really tightly correlated with a usage of SNAP benefits, etc. Yeah, I mean, the first question that we get asked all the time is can why why you don't have our location in eviction lab, right? Like people want more data and more geographic locations. And in that sense, I think feet is like really powerful because people themselves can do that work if they have the connections, if they have the capacity to do that. But I would echo what you all said already. I think we know we're starting to have more data about evictions, but I think the causes and the consequences. Those are the two biggest challenges. So what made people get to that place? We know that a big percentage is, you know, the inability of paying rent, but what why, right? And then the second one, the second part, the consequences. What's the outcome? Where do people end up after that? A potential connection, for example, with homeless management, homeless information management systems, the HMIS and things like that would be really, really useful. But also like there is data about overcrowding in the census and other things that we could interpret as, you know, the consequences of this. Yeah, absolutely. So we have a lot more questions that are running a little bit low on time, but just so just to close this out, I mean, I think, you know, with additional resources, I think a couple of things, both, you know, replicating the process that Matt just sort of walked through from data collection to data interpretation to creating a dashboard that is useful for local housing communities to be able to use and to sustain over time. It's definitely sort of from, you know, from beginning to end, being able to help jurisdictions with that actual process. And then also just, you know, in New America data kind eviction lab and working with localities, we've just learned a lot about sort of the facilitators and the barriers to working with this data and analyzing this data and so just being continuing to elevate those lessons. But for those of you who have, who are still with us, I, you know, we're going to close but just want to say, if you utilize the tool and have any feedback, there's a feedback button within the tool. So please share, you know, any of your experiences, if you run into any technical challenges, please, so that you know this tool was just launched last week. We want to continuously improve it continuously, you know, add features that work for users. And then also, we will share all of our contact information in a, in a post event summary with all, you know, all of the links to these resources. So you can contact us and we can continue the conversation but I think we would be really happy to also demo the tool to any housing communities that you think could utilize it. And so we will include that as well in any post event communication. But thank you so much for everyone who took the time to, yeah, let us share this resource with you and we hope we hope you find it useful.