Data Driven Business Intel with FRED and the @Federal Reserve Bank of St. Louis #DDRE26

Data Driven Business Intel with FRED and the @Federal Reserve Bank of St. Louis #DDRE26

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Aaron Norris: Welcome back to the Data Driven Real Estate podcast, the podcast for real estate professionals dedicated to driving business using data. I'm Aaron Norris, and along with co-host Sean O'Toole, CEO of PropertyRadar, this is Episode 26. And we get to interview FRED. FRED is housed within the St. Louis Federal Reserve and we are very excited to interview as we've been longtime users and huge fans for a number of years. This week, we've got Yvetta Fortova, she is the manager of FRED and FRED-family of products, including ALFRED and GeoFRED, which I didn't even know existed until this interview. We also have Maria Arias who works on the FRED team developing and maintaining the data update process, the data process on hundreds of thousands of different data series. We

cover everything on what that's like, and the different data sets that are available, how they inspire everybody from a kindergartener to a PhD, how to explore the data and make sense of it and release their own research, and how you could even become a forecaster and get worldwide recognition. You won't want to miss this week. So, hey, Maria, and Yvette, we really appreciate you being here today. And I guess the question I want to start with is why data? What keeps you excited about this industry? What do you love? Yvetta Fortova: Hello, Aaron, delighted to be here. Just before we start, we would like to say that the views expressed here are the views of our own and not the views of the, or the, of the Federal Reserve Bank of St. Louis, or the Federal

Reserve System. But data is very exciting to us. And we work on a FRED team, which is a team specializing in dissemination of Federal Reserve economic data. And really, data is very unique because we really like the fact that our website can deliver data service to users. And data can mean stories. Because when you look at data, you can see trends in data, you can see when the data goes ups and downs. And that always comes with an

interesting story. Sean O'Toole: I've been a longtime FRED user, you know, probably since it first launched, when did it first launch? Maria Arias: Yeah, that's a really interesting story, actually, officially on the web, FRED launched in 1991. But he ideal FRED actually started in the early 60s. So, the research director at the time his name was is Homer Jones. He wanted to

share some monetary data with other policymakers, but also with the general public. And so, he started disseminating a memo that contained these three data tables. And so, for a long time, it was just like a memo that was updated, of course, like once people received that they wanted to keep receiving it with an updated value every, every month or every week, however often this was. And eventually, in the early 90s, FRED became a dial up

bulletin board, and it's just grown from there. Sean O'Toole: Wow. Okay, so, I'm not nearly as early as I thought I was on that.

Aaron Norris: And when you say grown, can you describe sort of the vast amounts of data? Like in your library? Yvetta Fortova: Yes, FRED has definitely growing exponentially. We've, as Maria said, in 1991, when FRED first started, we had about 30 times series. And when you talk about time series, think about unemployment rate, or gross domestic product, those are the type of time series that were available. And then we've really, with technologies and with a lot of automations. In 2013, we reached our milestone

with 100,000, 100,000 series. And today, we are almost at an 800,000 time series that we continuously maintain and update in FRED. Sean O'Toole: Just again, I want to keep like help orient our readers, right? So, this is a basically a service of the St. Louis Federal Reserve, right, that makes that available. And it's at FRED.StLouis, tell, tell us where people go to accesses if they haven't before.

Maria Arias: Yes, FRED is a data aggregator, which means that we get data from public websites like government institutions, international organizations, and then some private institutions and academic resources. And we keep that up to date in our in our website, and so the way that this works is we're putting all of this data in one place for users to be able to combine and compare different time series from different sources. So, for example, if you want to compare employment growth and economic growth using parallel employment and the Gross Domestic Product, those are, both of those series are produced by different government agencies, the Bureau of Labor Statistics and the Bureau of Economic Analysis. But in FRED without leaving our website, you can plot both of those in a graph very quickly, and then compare them immediately. And again, for everyone who has not visited FRED yet. It's that FRED.stlouisfed.org.

Sean O'Toole: Perfect. Yeah, that's great. One of the other things that's, you know, you know, really impressive to me about it. And it's not, it wasn't perfect, right,,like so. And maybe you could talk about this, but you're getting this data from all these different sources, right, and now you're gonna lay it on a chart and compare it. And that presents, as a data guy, that presents some real challenges, right? Are you getting the same time slices, you know, quarterly versus monthly versus annually. And how much of your work goes

into just getting these things to align so that they, they work on a chart together. Yvetta Fortova: So, over the years, we developed a process where the notion of the time series is well defined in our back end. And really, it's all dependent on the frequency of the data. So, you can have data, like interest rates, there's

your interest rates, who are published, with a daily cadence, and the data is on a daily frequency. And then you can have data like labor markets, which are published by Bureau of Labor Statistics, and those data are monthly. And really, when all of this gets put together on our website, users can combine these data together on a graph. And there is a kind of like a magic in the, in the, in the in, the bag that happens that is able to translate that this data, while it's the treasure data, while it's daily, and the labor data, which is monthly can end up on one graph. And we have a highly interactive graphs, so users can see their values and download the data from the graph. And really, that is, what makes FRED very unique is the fact that we are trying to provide this data service to public and, and recognize the value in being able to give up to date information to our users.

Maria Arias: Right. And just to add a little bit on to that some of our tools that we provide, as well as, while you can add multiple series to the graph, you can also edit the graph right then and there. So, for example, if you want to compare, like you mentioned quarterly and monthly data, you can convert the monthly data to a quarterly frequency, as well. And so that's all the calculations are all done in the background. And then you can pick if you want like an average for those monthly values converted to a quarterly frequency, or if you want a different type of aggregation, things like that.

So, it can really allow you to create a customized graphs for whatever your needs are, or whatever data you're interested in. Aaron Norris: How did this, how did this line at the St. Louis Federal Reserve, or the other banks doing something similar, or they're just like St. Louis has got this.

Yvetta Fortova: So, FRED originated in St. Louis, and we are part of a research department. And as, Maria kind of diluted that, really the in the 60s, the big vision for Homer Jones, who was a, who was a research director, at that time, he really liked the vision of allowing data to be available to public and really, this product has since been organically evolving over the years with multiple other side products and creating this FRED, FRED-family of products. So, FRED is the mainstream of the data but then we have a mappable data in the in a GeoFRED and then we have also real time data in ALFRED so we're really trying to capture as much as much pieces and angles of the looking at the data as possible. Sean O'Toole: Somehow I completely missed GeoFRED like I've been a FRED user for I can't tell you how many presentations of mine have at least one FRED chart in it, if not quite a few. So um, but I completely miss GeoFRED. I just found it this morning. And I only got a chance to play with

it after Aaron mentioned it. And so, I don't know how I possibly missed that. How long is that we've been around? Yvetta Fortova: Quite a bit. We, and what GeoFRED is just a way for users to see cross sectional comparisons. So, it's just a way of looking at the data on a map. So, in comparing the States in

the US, for example, and if, you're right that it was developed on a side, and maybe it was not as well, as well provided to our users, but we have been trying to, over the years trying to incorporate the maps on the FRED website in a better way. So, nowadays, if users would be able to, would be able to see View Map button next to their graphs, that's an indication that they can also examine and then analyze the data on a map. And in addition, we also provide globes, images of globes below the graph, in the related content section where users can also see data in GeoFRED. Sean O'Toole: Okay, I was trying to do I'm sorry, Aaron, you go ahead. Aaron Norris: Visualizations are just very powerful. So, I have a

lot of playing to do. I too, have been a long term user of FRED and as pulling together different data from different governmental agencies has been very tricky. And then I found FRED and it was a godsend. I can't tell you how much time you

guys have saved me over the years. Sean O'Toole: We're total fan boys just to be clear. We're definitely your fanboys. Aaron Norris: Sean, I already asked Maria. I'm like, Where can we get those sweatshirts? Sean O'Toole: I know, I totally want one.

Maria Arias: Yeah. The FRED swag is available at the St. Louis, St. Louis Feds, the economy museum. Unfortunately, it's closed right now. But as soon as it open, we're expecting to have an influx of FRED fans coming to get some FRED swag. Aaron Norris: How many how big is your team, like when you're dealing with 800,000 time series? What kind of manpower does that take? Yvetta Fortova: Our team is, is relatively small, we have a team of about 10 people. And they consist of the developers who are working on making sure our website functions properly and developing new features and new tools to FRED. And then the

other part of our team, our data engineers who specialize in making sure our data and content in FRED is, is up to date. And because FRED is so popular, we also have a lot of help outside of our team within the research department to help us with other other things related to data and, and content. Aaron Norris: Do you have any idea how many users are on your site any, any specific time of year? Yvetta Fortova: Guess, our users fluctuate. And we have kind of like a seasonality in our, in our user, in our users. And that

is, that is correlated a lot that education, education and semesters at schools because one of our core users are academia, professors and students who utilize our product to learn about economy, money and banking. FRED is in a textbooks for students to learn about data and do homework. But overall, we do have over over millions of users annually that come to our sites from many countries, and are hungry for data and for information about data and what and graphs.

Maria Arias: Yeah, something that we think is really cool about FRED users is at any point in time, we could have a person who is just now learning about economics, it's their first time looking at a graph, as well as a PhD economist or even Nobel Prize winning economist on the website. So, again, we try to make sure that our features are easy to use, the data is accessible for everyone to learn about, but also that we have tools that more advanced users can take advantage of and also, you know, like everyone in between. So, we're, again really happy to to hear when people really like our product, and we try to keep developing new features for you guys out there who are using it to collect the different data that you need. Sean O'Toole: I personally think it should be you know, there's a whole class on it in high school, maybe elementary school, right? Like for every person before they're allowed to graduate before they're allowed to vote, like they should know how to use FRED, like, it would clean up half the crazy ideas that I see on, on Facebook and Twitter, and all the wrongheaded ideas out there. Because it's just such, you know, you can ask these questions rather than, you know, guess about them. And it's

just, it's really an amazing resource. Maria Arias: Right, even thinking one step back, we, one of our core missions is to promote data literacy. And that's something that is very important to us. So, just going back to understanding, you know, what is the frequency of this data? What is the ,what are the units, you know, how can you compare two different series, they have to be in the same units in order for you to compare apples to apples and not apples to oranges. So, we work very closely with another group in the research department. That is the Economic Education Group, and they create a lot of different materials from kindergarten all the way to high school and college level educational resources for, for students out there and teaching assistant teaching materials as well.

Aaron Norris: Really? Sean O'Toole: That's amazing. I didn't know that, that's, that's great to hear. Aaron Norris: I'm gonna have to find links to that. That sounds

really cool. Kindergarten? Maria Arias: Yes, down to kindergarten, there are some, actually there stories that are read out loud. And so, you could just sit your kid in front of a tablet and play this story that teaches them a lesson about finance, or education or economics, things like that, and you find resources at econlowdown.org. Sean O'Toole: Might have to start with that kindergarten, one for my 18 year old. Aaron Norris: And build your way up.That's amazing. Sean O'Toole: Build his way up? Yes. He's actually pretty good.

He's had to put up with me as his dad. So, he's not that bad. Aaron Norris: Now, somehow, I don't think he's gotten away with scot-free for sure. Sean O'Toole: No, yeah. Aaron Norris: Maria, you have to deal with a lot on the on the back end. And I really want people to appreciate what it means on the data side. So, what is your day to day look like when data is coming into the system? How does that work? Maria Arias: Right. So, for our process, currently, a lot of it

is automated. And so, we have developed a Python-based pipeline where we, when the data comes out, we reach out to the source's website, collect that data and process it and save it onto our database. And at that point, it's available immediately on our website. But we have a set of like notifications and things like that throughout, the throughout the day with these processes, making sure that everything is the data is updating successfully. And if there are issues, we would go in and check each like specific data set or release to see what what is happening. And then this is kind of for, like maybe 90% of our data is automated, updated in an automated way. And then we also, for some, like for the remainder

of that we either manually have to go and check to see if the data has updated on the source's website. Or maybe we wait to receive an email from subscription, that the data is available, things like that. So, there are a few kind of outliers out there that involve some sort of manual intervention to get the data updated. But kind of even before we get to that

stuff, we as we add new data to FRED, we have to add them to this data pipeline. So, that involves us, as data engineers understanding the data and how the data is provided by this particular source or the particular data set that we're adding, so that we can process that data and kind of standardize the data so that we can save it to a database. Aaron Norris: Who decides what data gets to be featured in the app? Yvetta Fortova: So, for us, we have lot a long list of data requests that comes from various places, our users, the data providers themselves, who would like their data added to FRED, and as we mentioned, our team is relatively lean. So, what we do

is we have internally have a committee that meets on a regular basis. And then we, we select data that we think are relevant to our users, and they are reliable as well from the originating sources. So, that we, we can continuously provide new content to users and and we can, we can, we can give our users additional data to work with. So, for example, recently, we have added weekly pandemic unemployment claims, which has been developed by Department of Labor. And we have also worked on addition of housing data from, from Optimal Blue and realtor.com, which are also available on our website. So, anybody who's interested, can go and find them.

Sean O'Toole: I saw one on GeoFRED it was market hotness. That seemed to be a real estate related. Maria Arias: Yes, but as a data set from realtor.com, as well, they have a set of indicators that they define as market hotness indicators. So, it's a different different topics that

kind of like how many days a house has been or like, average of how many days houses have been on the market in this region, things like that. And so, that's a data set that they put together and we publish in FRED. Sean O'Toole: On GeoFRED, it had very few counties populated from what I saw, yeah. Is that still just a work in progress? Is that the reason or did they just not have data on most of the country? Or do you have any ideas there? Maria Arias: Yeah, so, that's a we have their complete dataset in FRED for the market hotness data, they have the only the largest. I don't know how many I think it's the largest, like 100 MSA. And then I think the counties that are included in

that data set are from those MSA and surrounding areas as well. So, it's not a county by county for the US for this particular data set. Sean O'Toole: I happen to pull down County, if I pulled down MSA, it probably would have filled in a little nicer for those MSAs versus County. It's like only when the MSA maps to the county, would it work? Right? Maria Arias: So yeah, what depends on what data they have available for that particular data set. And yeah, I know that for that one. In particular, it's just the certain number of the largest MSA and then those surrounding counties.

Sean O'Toole: We have a lot of data users. And I want to come back to your your data pipeline, right. So, in that, in, for that data pipeline is, you know, I've used obviously, the website and the graphical tools, right? Folks that want to get access to raw data, is your data pipeline, open source? Do you guys have an API to the raw data? What are the, is there any options there, they've got to go do all the same hard work you did. Maria Arias: Right. So, the work that we do, hopefully will make everyone else's work a lot easier. We are doing the hard work of going to, you know, the Census and the BLS and all of these other organizations and parsing their data that is provided in different formats. And putting it all in like one

standardized format in FRED. And so, for all of you who want to maybe automate your workflows or things like that, we also have, we have a FRED API. And we also have an add in for Excel that you could use. So, that allows you to, again, automate your

workflows in different ways and combine it with the other data that you already have. So, if you like programming for the API, in particular, you just have to create a free user account and request an API key. And there are third party kind of wrappers or packages that are already out there that other people have created. And they just kind of wrap around the

FRED API and make it easier to use. And then that's in addition to you know, if you go to the FRED website, you can download the data as a CSV or an Excel file. And even if you've added multiple lines to the graph, if you download the data from that graph, it'll include all of the data that you have. So, that's also another way that, that you could access the raw data. And then Sean O'Toole: All the above love it.

Maria Arias: Awesome. And then you have for the GeoFRED data as well. If you want to download all of the say, like all of the county level data for the entire US, that's probably the best way to download all of that data is through GeoFRED for that particular map. Sean O'Toole: And all the data itself, basically at least is provided from you is open source, and people can use it in their applications and that kind of stuff for their license limitations at all? Yvetta Fortova: So, most of the data we have come from governments or sources or international organizations, and.

Sean O'Toole: Public record. Yvetta Fortova: Public domain data, correct. But we do have small percentage of data that comes with copyrights. And users will be able to identify those data because we have a note under the graph that specifies what the copyright, the restrictions are. And we also have in a notes, what that means

to the end user. And if they need any additional actions to utilize the data, but overall, from the whole scheme of data you have in front, it's a small percentage of data. Aaron Norris: I like to, Yvetta what you said about data telling stories. And part of that is, what I like about data is there's a trust factor to it. It's, it's just the data, the data is the data, data doesn't get political, it tells a story. How important is the data vetting process, when you consider new partners? I was, I had no idea that you were pulling in like a National Association of Realtors, which is an association group. What does that data vetting cycle

look like? Yvetta Fortova: When we are looking on, where like, what kind of sources, the data come from? We are we are looking on, how much data would be available to us? And also how, what the data look like how is the data constructed? If it's data that is academic data, we are looking for a ways to see whether there is some peer review work and or paper that has been published for construction of the data? And also, if there are ways that this data, how is the data disseminated all in all. So, in what way we, as a team can maintain the data, which is also a big piece. Because we are trying to look for ways of, on getting the data in an automated way. And cut down on the manual, manual work. So, there are pieces of of that. And

obviously, it also comes down with private sources to having proper licensing agreements in place. Aaron Norris: I know you've been on the research team before. So, do the data sources come in and then your research team gets a list of all the new data sources and they dream up what's next? What triggers some of the research that your team does? Yvetta Fortova: So, without our, within our team we do like our team does not do research. We're just solely doing breathing and living from FRED.

Aaron Norris: Yeah. Yvetta Fortova: But the economists who are in the research department, they, they pretty much have, have really a choice in whatever they would like to publish. And, and and write about. And their process. I'm not familiar with their process in details, but. Aaron Norris: That's okay. I was just curious, like, I can't imagine having access to 800,000 data series, and you just wake up in the morning like, 'Huh, what comedy work I got to put together today?' That's amazing.

Sean O'Toole: Do either of you have any favorite data series? Maria Arias: It's a, it's hard to pick with so many of them. But I didn't have a... Sean O'Toole: To look at in the morning or when the new, when the new drop comes that like, 'Oh, that's the one I want to see.' Yvetta Fortova: Yeah, we've definitely become kind of a data nerds over the years working with the data. And there, some,

some data are definitely interesting to talk about. For example, we do have data in FRED that is the longest series that we publish, and it's a historical data for population of England, that goes back down back to 1086. So, it's really depicting a plague that has, that impacted population. And then you have some really interesting data on orders of sinks, kitchen sinks and toilets during industrialization period, and the, the peak in indoor plumbing that has happened during that time. So, really, those are, those are our kind of

like, nice dimensions. But really, when it comes to favorites, it changes all the time because we, data is really interesting, when there are trends and when there are changes in the data as you as you deliver it to the to just spikes and drops in data. So, really looking at business cycles and seeing how the data is changes fit the business cycles or after the business cycles is, I think the most, the most interesting part of looking, looking analyzing the data.

Aaron Norris: Data is such a hard business too and I'm sure, as you've made more available, a lot of people are very demanding, I think one of my favorite series has always been migration. And especially with COVID, you hear a lot of, there's been a lot of talk in the real estate space and different data series to track not having to wait for a census, are you guys getting a lot of pressure to provide more series that are a lot more timely, instead of waiting for government, census and things of that nature. Maria Arias: We've seen a lot more demand for more, not not only more timely series, but also data that are more frequent or that have like a higher frequency. So, say daily or weekly data instead of like quarterly or annual data. And so, especially this year, when, you know, if you think about GDP and the state of the economy, you have to wait for the quarter to be finished. And then one more month for the initial

release of the previous quarters GDP to come out, right and ask like things are changing in the country, it's hard to tell what the state of the economy is at that point. So, we've definitely seen an increase in demand. Unfortunately, we're also kind of bound by, you know, we want to continue to provide high quality data to users. And we can't just add everything right away either. And so, it's, it's been kind of a, trying to see what the data providers are creating for like new releases, for example, the census created a, they created a experimental data set from their quarterly business formation data, they started calculating weekly business formation data. And as soon as they started making that available, we added that to FRED in the summer or early summer, late spring, something like that. And so, now there's this weekly level, business formation

statistics that has been really, really popular. Sean O'Toole: Is there what's the Geo-granularity on that? Maria Arias: Um, that is available at the state level? I'm not sure if it's also available at the MSA level. Aaron Norris: It's so, funny, Sean. We were just talking about that with Doug Duncan. Sean O'Toole: Doug Duncan, Chief Economist for Fannie Mae was just, he was just saying that's the one data set he would most like to have right now, but really, probably more down at the county or MSA level? I'm gona have to dive into that one, for sure. And we'll have to share it with Doug as well. Yeah, so, out of you, you know, you mentioned 800,000 time series, like, give us, give us some idea, you know, a guess is fine. But like, what's the fastest data set you're getting?

You're not like a Bloomberg terminal where it's, you know, sub, sub second updates on on stocks or anything like that. But the fastest is maybe daily, hourly? And then the worst is, is annual or is it some of them even less often than that? Yvetta Fortova: Oh, are you? Are you referring to how quickly we can get the data to FRED? Or what kind of data we have? Sean O'Toole: I think there's two separate questions there. And let's do both. Let's do the first one is how often, right? So, out of your 800,000? How many of those are annual versus quarterly versus monthly versus daily versus hourly, right? Just a guess. And then, and then, you know, she mentioned, Maria mentioned the, you know, some of these things have a month delay. And so, both of those things are really interesting issues, right, like, that I don't think people think enough about we have to think about it a lot, because we provide public records data on real estate, right. And in a small county, we

might go get the data every other month, right. And in LA County, which is the largest in the United States, right? We have stuff, you know, very, very quickly, so we get it daily. And, you know, we usually have it published to our site within just a couple of days, versus Alpine county might get it every two months still publish to our site within a couple of days, but there's big differences there. So, can you speak to that

kind of distribution among your datasets? Yvetta Fortova: The data we have in FRED are anywhere from daily, weekly, monthly, quarterly, semi-annual annual, and then we have some five year frequency data. And really the distribution among what kind of data we have in FRED is more or less, we have mostly, most data for on annual frequency and then it it kind of goes down from annual to quarterly, monthly And daily. So, that is kind of like the distribution of the data we have in FRED. But then there are also aspects of how, how, how the data updates. So, when we do our updates, let's say that an

employment data, the employment situation that is published by the Bureau of Labor Statistics is published on the first Friday of every month. And it comes out around the 7:30 Central time. So, we really are trying to hit the, the update process, immediately or minutes after the data is released. And then obviously depends on the bit on the size of the data itself. So, if employment situation has couple 1000 series, it takes some time to get all the new information into FRED. But we are trying to optimize our process to really get into it in minutes or from what the actual release of the data happens. And

then there are also aspects of the data itself. So, when the data is released, there are there is the lack of the information, because what happens is, first Friday of the month, there is a publish publication of the data. But actually, the data that's published represents the values for the previous month. And then that goes all the all the way back down to the origin eating agencies who are working on the, on obtaining the underlying data, and perhaps the micro data. So, they need time to get a good size sample of the data,

which then allows them to model the data and make the aggregated values available. So, those in most data, if we have in FRED of work on on this typical lack where you're not going to see data for December, monthly data for December, if the December isn't over yet. And again, it comes back down to, you know, the originating agencies having time to prepare those estimates. On the other hand, we also have a little bit of advanced data or data that is forecasting that are looking into the future. Which users can also also use to to look at what the data will look like. And last not but least because all of this

process of disseminating those data and publishing data is really based on a lot of times on surveys and on incomplete samples. That's why agencies over time, as they are publishing new values, new monthly data, let's say they are also going back and revising existing data. And for that reason, we also have ALFRED, which is an archival, FRED. Aaron Norris: Interesting.

Sean O'Toole: Yeah, And that was Alfred was another one I wasn't familiar with. And, you know, Aaron mentioned this morning when we were doing our pregame, you know, prep. And, you know, he mentioned, he had a data series that changed quite dramatically, you know, and one of the things that he was following and, and he was able to find that the historical data and ALFRED and see why, you know, see that difference. So, that's, that's pretty cool that the archival piece too, valuable.

Yvetta Fortova: Yes, I think the the best example that we like to give is from gross domestic product. So, if you think about gross domestic product, the data itself, is a quarterly frequency data, but then the values are annualized. And then the data updates monthly, and then the values to revise could revise every month. And on top of that there, there is an annual revision that happens every year in summer. And on top of that, every three to five years, there's a benchmark revision which may change everything under the GDP. Aaron Norris: I did not know that, wow.

Sean O'Toole: GDP doesn't mean anything. It's just a random guess. Just kidding. Aaron Norris: I.. do you find that your work has inspired states to get a little bit more serious about collecting data. I

noticed just in California as an example in the last year, I've noticed a lot of data collection sort of budget line items in legislation talking about digitizing and starting to track things. And can you point to some of your work being so helpful that states are like we got an upper data game? Maria Arias: We would like to think that we inspire other government and public institutions to improve our data collection. In some cases, where especially like smaller government institutions have reached out to us about, you know, having their their data and FRED and it's not necessarily available in a machine readable form, we've been able to work with, with them, and even some of the academic data that we've added, that seems to be pretty popular. And FRED, we've been able to work with them to help them put their date make their data available on their website in a, an easier form for us to parse but also for other people. So, like machine readable forums, standardized tables, things like that. And so, we have seen a little bit of that. But we don't work with like, we don't work with every government institution out there that we collect the data from, because again, all of this data is publicly available. So, we just

collected from their website. But we would really hope that more and more, especially smaller government entities, to collect their data and make their data publicly available in an easy format, because it's not only for us to provide a service for other users, but also, you know, everyone around them, and in your communities, you know, you know, what's important to your community. And you know, what industries are what drive the community forward, things like that. And so, if the

government's local governments were able to help collect that data and make that data more available, it would be easier to also analyze the state of the economy and these kind of smaller geographical regions, like you were talking about earlier, it's really nice to have county level data, it would be even nicer to have it at a smaller geographical level for everyone who's doing research and trying to understand what's happening. Sean O'Toole: Our company, the one that sponsors, the Data Driven real estate podcast, PropertyRadar, really only exists, because of how difficult it is to get public records data on real estate assessors data, recorder data, you know, etc. And I would, it would, it would make me so happy to shut this company down, because the data was directly available. On an easier format, like out of all the things that we spend money on as a government, you hear billion dollars here a billion dollars, they're like, why your group and the group's in all these different levels aren't funded to the tune of billions of dollars? You know, I have to say, I was a little dismayed to hear how small your team was, and I kudos for what you guys accomplished with that team. But, you know, if you ever need

somebody like you to write a letter on why you should get more funding, like you can count me in. Aaron Norris: Yeah, the actionable side of data I serve on the county board for 211 out here, and you know, a lot of counties will do is to Sean O'Toole: What is 211 Aaron? Aaron Norris: Oh, sorry, 211 is a Health and Human Services hotline. It's, it's nationwide, it's sort of like the 411 for help. It's it has a lot of nonprofit data government

assistance programs. And I think why I joined was the data is that you don't have to wait a year for to spend $100,000 on what people need in the community. This 211 hotline is tracking the need. So ,30% of the need in our community for

the last three years has been consistently been housing, you don't need to spend $100,000 in wait a year, it's almost immediate. So, it's, it's data does tell a story. And sometimes it's really important because government entities can redirect resources, where it's needed, instead of wasting time, and you're a year late. So, I'm just curious, what are some of the data that you're most excited about in the last few years that you've seen cross your desk that you wish people knew about? Maria Arias: So since. Sean O'Toole: 800,000, there's too many. Maria Arias: I'm just trying to think of what we've added this year, um, something that we did this year, and it's real estate related, so I definitely wanted to bring it up is the Optimal Blue mortgage market indices. So, they have mortgage data that is broken down by different FICO scores and by different types of loans. So that's a, again, another private institution that

reached out to us to have their data added to FRED and it's a, it was added this year. But then we also have some other like you've mentioned the weekly pandemics claim pandemic claims data. That was a really interesting one. We also added daily FOMC target rates that were digitized by one of our economists at the St. Louis Fed and some of his co authors going back to the early 1900s. So, now you can get, like FOMC, target rates starting 1900s. And then like slowly put the time series

together, up until present day. And then what else? Oh, there's another one by the a group of economists this one and this is going back to kind of some of the changes that we've seen this year have more demand for high frequency data is a group of economists created the weekly economic index by last names of Louis Martin stock. And this is a kind of like a now cast, but not necessarily an outcast, per se. But they combine several high frequency data that are available at the national level, and they create like a GDP forecast more or less at the weekly level. And that is actually updated twice in a week. And so, that's been really interesting to see as well. And

again, just kind of making the, kind of having like a more frequent update of state of the economy and keeping that conversation going. Aaron Norris: Is it I haven't looked yet, HMDA data I know is a I'm a, I have my mortgage license. And the amount of detail that the CFPB has mandated that we start collecting at the loan level is quite expansive, is that available on FRED yet? Yvetta Fortova: Oh, we do not have humped up data in FRED. Aaron Norris: Okay.

Yvetta Fortova: But I would say for anybody who is interested to learn about what data we have added to FRED, we would recommend to sign up for FRED newsletter, which is in bottom of our web pages, and we let users know what new changes in data and what new data we edit that way. Maria Arias: A new feature sets... Sean O'Toole: Right there, that's, that's, that's my tip of the whole podcast, right? Like, because I've been a FRED user forever, and I'm not signed up. And I don't know why. So,, I'm signing up right now. Aaron Norris: I'll make sure to post some extra links to to make sure in the show notes that people can find this stuff really easy. So, I think in the real estate space, there's a lot

of like exploring different economies, and maybe some people they've never approached FRED before. And so, maybe we can give a little bit more industry specific. And I know this is your personal opinion, if you were going to explore a state that you didn't live in, and you were interested in, you know, the economy, real estate, I don't know what kind of things would you explore in FRED people might not know exist. Yvetta Fortova: So, definitely, for someone who is new to FRED, we would say, to go and try to search for information and see what kind of data we have available for, for given states or MSA's or counties. On the regional level, we'd, and they will quickly realize that we do have a lot of major economic indicators like labor data and gross domestic product. But then

we also have data on property prices, like house price indices, and we have rates on mortgage rates. So, there's really a wealth of information available. And it's a, it's a really matter for people to go and try it and see what we have, you know, from housing stars and consumer price index, to prices and wages. And it's kind of like, when you go shopping, and you feel like you've really good, got a good bargain at the end of the day. So, that's really what we are trying to

give users with FRED. Maria Arias: Right. And then I would just add on to that, that if you find some graphs that you find really interesting, or that you would like to come back to. This is just kind of a plug for some very useful FRED features. And especially if you don't have a FRED user account yet, right at the bottom of the, of the graph, you can save graphs to your user accounts. But then you

can also add graphs to a dashboard. So, as you add graphs of say, a similar topic that you are saving to your user account. You can also put all of those graphs or maps onto a dashboard and add other like snippets, like notes or like a single number or something like that. And then all of these graphs can be saved so that they are updated automatically. So, the

next time the data comes out, you just go back to your dashboard or to your saved graph, and the data is automatically updated for that. So, this is a very useful feature that if you're not familiar with FRED, or if you don't have a user account, I highly recommend you check out, especially again, if you have these kind of several graphs or indicators that you'd like to go back to. And then together with that, you can make your dashboards public and share them with your colleagues or share them with other people in the industry, to just, again, simplify sharing data and sharing content.

Sean O'Toole: But, you know, one thing I haven't looked at, and I'll just ask is, can you embed? Can you get like an embed code for those graphs, so you can embed them in a website or whatever, so that they're constantly updated on your site as well? Maria Arias: Yes, just below the graph, there are some shared links, and account tools. So, under the account tools, that's where you can save those to your account. But then the share links, you can share a URL to a graph. And so, whatever modifications you've made to the graph, and you share that, using the URL provided under that button, the person who accesses that will see exactly the graph that you created. And then same with the embed code, if we provide like a pre created snippet that you could just put onto your website and embedded that way. Sean O'Toole: In my, you know, one of the things I really like is the, the bars that show recessions, right, um, and, excuse me here, if I like, don't know, that, in terms of those bars, that kind of underlie right recessions is, is that the only one or there are some others? Yvetta Fortova: We only have recessions at this point on the graphs. And there are other ways you can, you can take those

recessions off from the graph, if, if there's no need for them. But we also have a way to manipulate the data of it in the graph itself. So, for example, if you have, if you have a data, you can then index the data to different time of the year, or different period in time and, and it will set the values to 100 of a given period. And then you can see changes to the in

the data. So, that is also one way how someone can kind of take the data and, and make it work to whatever they are trying to save at the data. Sean O'Toole: I want to just expand on that a little bit to make sure our folks understand, right. So, sometimes you get these data sets that are wildly different in their values, right? Instead, in some, you know, one way to do that in charts to show one value on this side, one shot value and this side. But when you're looking at the relative change over time, you guys allow you to set a date at which you basically normalize them all. And then you can see how the change relative to each

other from that, even if they have completely different scales. And that's a super useful tool. I gotta say, the one thing that I do a fair bit is, is, is underlie those events besides recessions, right, like, one of the things that that folks in the real estate community worry about a lot is changes in administration, and changes in the makeup, excuse me, of the, you know, like the house in the Senate. Right. So, like, everybody obviously is worried right now about this or thinking about this Senate race, and Georgia. And I'm sorry. And so, one of the most interesting ones I've done over the times is I go back and kind of like the recession bars. ,I you know, it's republican versus democratic president right, and then a divided Congress versus, you know, the same Congress and is it all three? Or is it one in two like that, like, I would love some more of those, you know, different things, that particular one, you know, administration would be great, but I'd love to see some more like that. Because I do think

that is that's a really, you know, a really cool feature. Yvetta Fortova: Yeah, that sounds really interesting. So and thank you for the suggestions, suggestion we can, we can look on. But what it will look like and how we can, how we

can approach to have a better options for users and then maybe have ways for displaying different administration's as you said, and one more thing that I also wanted to add is that if, if it needs to be users can create draw lines on the graph, as well. So, there is a way to create user defined lines. So, that is probably the closest we can get you with the, with the situation's you're facing. But definitely, it's a, it's a place for improvement for us, so thank you. Sean O'Toole: It was an eye opening to me on, on, you know, what we think about in terms of which party is fiscally conservative and which one isn't. But it was not what I

expected. Aaron Norris: And it's good to tell that story in data, it makes it less political, well, maybe it doesn't. But if you're a real estate in real estate, and you've never explored some of the tools, I really liked the recession, because when you go into an area, and you can see a recession, and how that local economy, unemployment, household income, median price, really fared through like a recession, it's really interesting to drill down as you're sort of making moves, or you're helping clients that want to move out of state and they're exploring a new area, I just the easiest thing to do is go on the website and type in the name of a county and just start playing and see all the different data series that are available. It's it's almost overwhelming. Just have to get in there and explore if Sean, do you have any other favorite ones that you use? Sean O'Toole: Oh, you know, the biggest one I use the most often is I look at the different types of debt, right? So, mortgage debt, corporate debt, total, you know, the the federal debt, you know, etc. student debt is just a student that that chart is

really scary bad, like, you know, especially if you take all of those different types of debt and put them at 100. You know, say and 2000. And look at how they change over time, it's, we clearly have a huge student debt problem. And I know you guys probably can't comment on the, on, on those things. But that's, that's probably one of my favorites that I look at the most.

Aaron Norris: Well, we are about out of time, I'm trying to think if there's any other things that we should cover, like maybe new features that you're working on, that you could share. Yvetta Fortova: So, we are working on working on surprises for 2021. And next year, FRED is going to be 30 years old. So, we're going to be looking in some ways how we can make FRED more mature looking. But there are definitely going to be more

data edit throughout next year. So, that comes up that we tried to do that on a regular basis. So, so, that we don't lose interest in you guys. And, and other other aspects here, we are going to try to look at is, we would like to improve our search abilities in FRED. And we are looking into ways how we can expand on providing users with some industry break down on the data. Aaron Norris: Sort of categorized things and in buckets, or something? Sean O'Toole: I would love I mean, with 800,000, this is a big ask, but like a little deeper descriptions on each one.

You know, because a lot of times you'll see like, you know, you'll type something in that you're interested in, you know, inflation or whatever. And there's 30 or 40 time series, and you're like, Oh my gosh, which one of these do I use? I just wanted this simple thing. And it's that piece, the number, I mean, the number of series can sometimes get a little overwhelming in terms of which one should I use. So yeah, any, any suggestions there on how best to parse that when you get and you're like, Oh my gosh, there's 30 ways to look at mortgage debt or you know, whatever, like any any suggestions on how people can get to the right one and not make bad decisions? Yvetta Fortova: Oh, we, one of the things we tried to do and in our search is to really work the most popular and relevant series to the top of the search results. So, for novice users, that may be an easy way to, to know that based on their search, that the, the series at the top are are going to be the, the best to use. And also, we try to provide additional filtering options on the side of the have the data and to subset the searches. But if all else fails, we really and there's really not

a good description in a data. We really tried to ask users to see if they will be able to go back to the originating source and, and, and see if they have some other descriptions to to make the decision of which series is better. Sean O'Toole: I do that a lot I go in. And it's a you know, it's an amazing chance to learn, right? Because you see others these different ways of looking at it. And so, then I'll take the time to go research those different ways. And I come out smarter at the end of the day. So, it's not it's not all all

bad the approach you take, and Google's your friend on that right Wikipedia and those things for sure. So great, Maria Arias: Right. And part of the filtering options as well is if you can filter by source. And so, if you know you want, say inflation or consumer prices by a particular source, you can filter by source and that would sometimes help you find the right one or close to what you're looking for.

Sean O'Toole: What else did we not cover that you guys would like to to share or mention before we wrap up. Yvetta Fortova: Um, I would say for anybody who is interested to do some playing with the data, we didn't mention that we also have a forecasting game called FREDcast. And we, if you have a user, have a user account, you also have access to FREDguests. And what it is, is, users can be part of a public leak, and they can set up their own leaks. And they can create forecasts for,

for economic indicators. They can forecast unemployment rate, GDP, payroll employments and inflation as a CPI gross, and every every month, the score gets calculated, and then you're ranked among other people playing. And you can see how good you have scored and what your average error is towards other players. Maria Arias: You earn public recognition and also earned badges. So it's pretty fun.

Sean O'Toole: I'm all about the badge, I was a boyscout, so I had lots of badges. Aaron Norris: That's amazing. I'm so glad you brought that up. Maria, do you have anything else you'd like to add? Maria Arias: Just go in there and search for whether it's your topic that you're interested, or the region and the country or the world that you're interested in and see what you can find. Yeah, there's lots of different ways to search for data in FRED, right under the big search bar, and in the middle, there's also Release Calendar. So, you can see what data releases are

scheduled to be released today or this week. And then you can also search by category. So, that's kind of all of the data in FRED are grouped into different categories and that's also a good, a good way if you're interested in a particular topic to kind of go down the different breakdowns of the data. Aaron Norris: Fantastic. Well, thank you so much for your time today. This has been a lot of fun. Maria Arias: Thank you so much for having us.

Yvetta Fortova: Thank you. Sean O'Toole: Yeah, thank you so much, really appreciate it. Like I said, we're definitely fans, so keep up the good work. Aaron Norris: Thank you for listening to the Data Driven Real Estate Podcast, you can find show notes and links to some of the resources mentioned in the show at datadrivenrealestate.com. Click that join the community, and you'll be forwarded to the PropertyRadar community where you can ask questions about the current show and even see upcoming guests and ask questions there. We'd love to

engage with you in the community. So check it out. Please don't forget to like, favorite, subscribe and share on your favorite platform where you're listening to the show. It helps us out a great deal. Thanks for listening, and we'll

see you next week.

2020-12-29 06:47

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