How to Analyze Rent Data by Zip Code | Part 2
In this video, I'm going to show you how to use data that we previously got in our last video for ZIP codes. And what we're going to do here is visualize them using Tableau will be able to quickly see some of the more expensive zip codes within our area, in this case, Tampa, Florida.
Unknown Speaker 0:00
Are you looking to get rental data for your market, whether it's by county or city, but you want to get even more granular, you want to see what neighborhoods to invest in? Well, in that case, we need to look at the zip code level. In this video, I'm going to show you how to use data that we previously got in our last video for ZIP codes. And what we're going to do here is visualize them using Tableau will be able to quickly see some of the more expensive zip codes within our area, in this case, Tampa, Florida, as well as see year over year changes, so we can analyze and understand what emerging zip codes are in our area. My name is Ariel Herrera with analytics aerial channel, where we bridge the gap between real estate and technology. I love data driven solutions and bringing analysis to life through visualization. If you do too, then please subscribe to my channel for the latest content, as well like this video if you want to see more visualization solutions, including Tableau to Alright, let's get started. In this video, I'm going to be walking you through how to create this exact visualization for your own county. And you need to make sure that you've already passed step one to get the data for your zip code. In some case, if you don't like coding in Python, and don't know how to but you want to be able to take on the second step, then please reach out to me directly so that we can work out a way to get the data for your own zip code so that you can follow through in this tutorial. First, we're going to go over what this dashboard is. Second, we're going to create your account for Tableau Public which is free. And third, we're going to create the visualizations that make this dashboard. So let's look at what's here, we have two different sections. And in our first section, this is more at the macro level. So we're looking at everything for the whole entire county of Hillsborough County for Tampa, Florida. And if we backtrack a bit, we could see where Tampa, Florida is Tampa, Florida is on the left hand side of the state, which is left of Orlando and considered Central Florida. So when we zoom in with this chart does is it takes the average rent by zip code. And we could see our legend on the right hand side, the average rent goes from 1800 to $4,900. And we're looking at this particular month, and three bedrooms, we could see here the most expensive market is 33629 zip code, which is kind of by the Soho, as well as Hyde Park area, with the average rent for a three bedroom at $4,900. Then we have some less expensive zip codes towards the outside of the city, which makes sense because these are more suburb towns. But hint here is that for Tampa, a lot of the southern towns are growing. And they're going to be our focus as well to see which ones are growing the most. On the right hand side we have total rentals. So when we get our data from real Tmall we have information from the endpoint, the endpoint that we looked at in the last videos rental market data. And the data that we received was for our zip code, we got average rent, minimum rent, Max rent, and total rentals. We also got the same exact detail for studios, one bedroom apartments, two bedroom apartments all the way up to max five, but we're just looking at bedrooms from studio to four bedrooms. So if we go back over to our dashboard, we can see here that there's a lot of rentals in this zip code, as well as the zip code below. This can help us to gauge supply and demand. Next, on the bottom, we have the option to select different zip codes in our area. And total we have 45 here, I just selected five. And we could look at year over year trends for these zip codes. We could see which one so in this case, 33613 had a 78% increase in the average rent year over year, this may signal to us that this is a zip code that is hot, maybe there's new construction that's been going on maybe there's new businesses that have entered, and people are flocking to living in this zipcode. So this could be a potential investment opportunity. And it's really important in general, not just to say, Hey, I'm going to invest in Tampa city, Orlando, Detroit, but to know what zip codes you're looking to focus on because as we know, you can walk a couple of streets down a block and it can be a completely different neighborhood. So be aware of that. Lastly, on the bottom, we're going to be creating ZIP Code trend analysis. So let's say we were interested in the zip code 336 10 We could look at a breakout by bedroom. How the average rent is month over month, we could see here that in the month of May, to June, there's a large increase in rent. And this is typical because a lot of people turn over during these months. So we'd see a lot of landlords charge a higher rent for the newer tenants, then we could also look at a breakout of total rentals by bedroom to see if there's maybe too much supply or too little supply. And we could see the year over year average rent increase for this one particular zip code. We could see for this zip code, the type of bedroom that has risen the most in year over year rental price is one bedrooms. So maybe a lot of people that were coming down from northern states to the south to Tampa, particularly were coming by themselves, maybe a single person or a couple. And this is why we might see that one bedrooms are in high demand and have had a year over year increase for this particular zip code. Great. So now that we understand what our dashboard is all about, we've mentally seen what we want to create. Let's go create that now. First, you're going to go to Tableau, Tableau Public is free. There's Tableau Public, there's Tableau online, and there's Tableau that you can download to your desktop, make sure you go to this specific one. Once you do, you're going to see that you have your own profile, where you could start creating your own Tableau workbooks. So go to your profile. And here you're going to see any visualizations that you've previously created. You can also favorite other visualizations that other people have created. If you go to discover, you can also follow others and obtain followers as well. Now we can go to create a vis for visualization.
Unknown Speaker 6:49
And here, we want to connect to our dataset. So in the last video, we got our data for all the zip codes within the Hillsborough County area. Again, please follow that video. But if you just want the data itself, reach out to me directly, and we can work something out. So what I'm doing here is I'm going to my historical data that we obtained, and I'm going to drag it into this box here. Now what Tableau is doing is it's processing our file. And in our Data Source tab, we now can see that our CSV file is now loaded. We also have all of the columns on the left hand side. And we can update this to get a preview of what's in our dataset. We have information on our zip code. We have many zip codes here, all the zip codes for Hillsborough, but for each ZIP code, we have historical data. So in this case, all the way back to 2020 of April, we have the number of bedrooms. And for each bedroom type, we get the average rent, minimum rent, Max rent, total rentals. And in this case, we're not going to see it for this month, because this month is April. But if we look at previous months, we could see that we have year over year changes. So we could see last year what our bedrooms was, or average rent was. And we could also look at our new features that we added, which was Z score, year over year average rent, year over year total rentals. And then we ultimately at the end of the last video appended the information that we got from the GitHub file where we have our state name, our county name, our city name, and whether it's a valid zip code or not, we have our dataset. So now let's go create our first visualization. So we go to sheet one. In order to do this, each time we're going from data source to sheet, we're going to have to wait for Tableau to reload our sheet which is normal. So if you see that, that's completely fine. Now we want to do is recreate this exact chart first. So the one where it was average rent by zip code. So we take our ZIP Code column, we're going to put this as rows. Now for each ZIP code, we want to get the average rent. So let's put average rent in here. But this average rent is huge 200k. There's no way that's because it's aggregating everything for all historical data. So for the two year timeframe, let's change this now. Let's go to our date. And our date right now is in a string format, which we need to transform into a date object. So we're going to duplicate this date. And we're going to rename this to month date. And here we're going to convert or change this into a date. And now we can see that little part on the left hand side has now changed to a date. We can go to our filters and select relative date. And Tableau is really smart. So if we just select that we want to get the lay Last month, we can select this month, and click OK. Now we only have data for our particular month. But we still see here that the rents are way too high, we know they should be between 1000 to 3000 the most. So in this case, we realize that our bedrooms are all being considered. So let's go to our bedrooms. And our bedrooms right now are in a number format. But we don't want them to be, we want them to be single attributes. So we're going to duplicate this. And we're going to modify our bedrooms column, change data type to a string. And here, we could rename our previous one. So our previous column that is a number, let's call it number of bedrooms. Then for our new column, we could just call it bedroom. So let's just remove the copy piece. Great. So now if we take our new column for bedrooms, let's put this as well as a filter.
Unknown Speaker 10:59
We could select all values. And let's just go for three bedrooms as of right now, and click OK. Now, this looks a lot more like it for each ZIP code. We have for this month what the average rent is for a three bedroom apartment. But we want to see this in a geographic visual just like this. So if we go to show me on the right hand side, we can see all the options of how we can visualize this data, we're going to select the maps view. And what's awesome is that Tableau automatically identifies that our zip code, which was our columns, or rows beforehand, is an actual geographic mapping. So it automatically creates this map for us. And now if we highlight and hover over, we could see that same exact chart we had before, and what the average rent is per zip code. But sometimes we don't know what the actual city is behind the zip code. So let's go to our city, which is over here. And let's add it as a tooltip. So that when we hover over, we could see what the city is. And if we look over here, now we can see that this city is Lithia, which is a suburb outside of Tampa, we can see this as reskin, Apollo beats Gibson 10. And as we go more inner the city, they're all going to say Tampa data set right now our visualization is labeled as sheet one, let's change this to average rent by zip code. We can see that this has now changed up top, and our visualization is complete. On the right hand side we have our legend, so we can see that the darker the colors are, the higher the rent is, you could always adjust this in the future. Next, we want to create that second chart, we want to see what are the total rentals by zip code. It's very similar to the previous one. So instead of recreating it from scratch, we can just duplicate this sheet. Once we duplicate this sheet, let's rename it, it's going to be called Total rentals by zip code. And we have right now that the aggregate is the average rent, let's go to what we want, which is total rentals, we can highlight this row and drag it right where the average rent was. And now we can see these values have changed. If we hover over we see for this particular city in Tampa the zip code, there's 199 Total rentals. Now this is great, but it's going to be very confusing when we look at these two charts side by side and they're both in the same color scheme. So let's change it for total rentals, we could do on the right hand side by the legend is hit the drop down and edit the colors. You can make them as custom as you'd like. I'm just gonna go for green for simplicity. And now we have this chart complete. We can add these to our Dashboard to get started. So let's go over to dashboard, we can see the sheets on the left hand side and we can drag them in. So here we have average rent. And we could drag in on the right hand side the total rentals. And then we see our legends on the side. If we want to expand this further, we can go to desktop and increase the width by just clicking up. And we can also adjust the height which we'll do later. But now we have these two visuals ready. So let's go to creating an average rent by bedroom. So if we go back here, we have year over year average rent increase. Now we have average rent by bedroom. Let's actually start with this one first and it's gonna go underneath our current charts. The goal of this chart once again is to see how red has changed across different zip codes. Let's create a brand new sheet. move this forward and we'll move dashboard to the front. So for this sheet, it's going to be called year over year average rent increase so we could rename it then we're going to want to see a line graph. And we're going to take our date column here and put it into our columns. Let's modify this to be month. Now for our rows, this is where we're going to have our average rent. So let's take average rent here for year over year, and place it where Rose is great. Now we could start to see that we're developing a line chart, but we want it split by zip code. So we could take zip code here and put it by color. So we could split it, we get a ton of zip codes, this one probably has some sort of bad data, where it looks like rent increased by 18%, which is highly unlikely. But as of right, now, let's just see how we can filter on a select amount of zip codes. So let's take zip code and put it in our filter.
Unknown Speaker 15:58
And here, I'm going to select just a couple. So let's unselect that, and we go to three 610 336 10, we could select it here, and just select for more afterwards. And you could select whichever ones you want for your own city. Now if we hover over, we have one more thing we need to correct. Right now we're looking at all bedrooms. So let's take bedrooms. And let's move that up here. And make sure we're filtering on just one single bedroom. So here we'll just do any rentals that are three bedrooms, and click OK. Now we have this chart that mirrors the same chart that we had over here. Great. So now let's go back to dashboard. And you could actually color your sheets down here and dashboard. So I'm going to right click and hit color and make this a dark blue. That way I can visually see when to come back to this part, I'm going to also color these and just make these yellow. So I know that these are all similar sheets, because they all are going to be in the first half of our dashboard. So in dashboard, one, we can now take the year over year average rent increase. And we can put that at the bottom. Now we see our chart here. This is starting to get really closed really tight, because we're adding a lot of charts. And we haven't really expanded that much for our dashboard. But we'll figure this out in just a moment. Let's finish the last sheets that we have left. This is going to be three sheets that are fairly easy to create. And they're all related to zip code trend analysis, I created a new spreadsheet called average rent by bedroom for ZIP code. What we want to do is also modify the previous column that we have for bedrooms, we need to make a minor correction here by converting this datatype back to a number hole, and then converting it to discrete. Next, to create this line chart, we're going to take the month date column, we're going to convert this to be by month. And then we're going to take the average rent as our value. Now we want to break this out for a single zip code. And in my example, I'm going to select 336 10 and click OK. Now I want to break this out further into bedrooms. So let's drag bedrooms into color. Then modify bedrooms. So we're seeing it as a dimension. And now we could see it this is broken out bedrooms, from studio all the way to four bedrooms for our givens of code. But what is our zip code, we don't want to have to go to the filter each time to see it. So let's go to our sheet. And let's add our filter here. If we do insert, and we could put zip code, and click OK. Now we can see that the zip code automatically populates once we enter it here. And if we like we can actually change the colors for means a little bit too drastic. So let's modify here edit colors. And we can look at different palettes. In this case, I'm going to go for Tableau 20 and click Assign palette. And now we have a little bit for me more friendlier colors to look at. Great so now let's replicate this for the other three charts that we'd like to create. So total bedrooms and your your rent increase. So we can do is make a duplicate of this. Let's change these colors since they're not as related to the other sheet by clicking green. And let's change this name. It's going to be total rentals. And all we have to do here is take total rentals and replace those with average rent. Now for our last sheet, let's also make a duplicate we rename it to year over year average rent increase. And what we change here is just Making the year over year average rent and replacing it with this value of top. Now we have all three of our sheets that we wanted to bring in. So we can go back to our dashboard. This is pretty small right now. So let's actually increase the size and make this a whole 1000 pixels larger, stretches out our previous graphs automatically. Now let's add in the new ones that we like. So we could put this towards the bottom. We could see here that we need to do some adjusting as our charts have gotten really small. But first, let's add a header. So I'm going to take text here and put this up top. And let's call this rental analysis by zip code.
Unknown Speaker 20:52
I formatted this a bit by increasing the size, as well as making this white format, white font, and now you won't be able to see it. But if we go to layout, we can change our background to be darker. So let's select a gray. And we could see our header could move this up, then these charts are also pretty big. So we can move this up as well. The zip code here is associated with this chart, but you wouldn't really know it by looking at it. And this bedrooms over here is associated with these charts. So let's move this over by dragging it to the right hand side. And for this, we're going to do the same thing. So let's go back towards the bottom, go into back out a little bit and then bring this to the side, we're starting to get there. Now let's take our previous sheets, and drag them underneath so that we have bedrooms on the right hand side. And this chart is way too big. So let's make this smaller. And now we're starting to get a better view of our data. We can keep adjusting this a bit more. But for simplicity, I'm going to stop at this point of just doing formatting and show you how to quickly do filters so that you can look at this data across different zip codes are actually up top, we're looking at three bedroom apartments, but we wouldn't know it by seeing it. So let's add in that filter. So we go here to our chart. On our drop down, we put filter. And we state that we want to look at the number of bedrooms. I actually don't see this right here. But it could be because we changed our bedroom column a bit. And I think that's it. So let's take our bedrooms back added in. All values, three bedroom. Let's do the same for our other sheet two. Now when we go back to our dashboard, we'll be able to see in the filters that we have bedrooms. And in this case, we don't want to be able to select all bedrooms, we just want to have one at a time. So let's go here and click single value as a list. And if we select, say two bedrooms and said, We're going to see this chart on the left hand side update but not the right hand side. So in order to get them both to update, we're going to click here and apply to worksheets, we can apply to the whole data source. But in our case, we don't want to do this, we just want to have it for select worksheets. So let's also click that total rental should update. Every time we modify this filter. Click OK. And now we could see as we change, say to one bedroom apartments, we're able to dynamically update both charts. Now you can apply this to the rest as well, so that you can have a filter for ZIP codes, as well as the codes up here too. For simplicity, I'm not going to go through all of that, you'll be able to either create the same exact dashboard that I have here or modify a bit for your own needs. So I hope this has been super useful for you to understand how you could use a free tool like Tableau Public, where you can quickly put visualizations together from a dataset without any code. If you'd like to see more visuals like this and using Tableau Public than please leave comments below. Or if there's other graphic libraries or tools that you'd like to see me try out. Please leave that in the comments as well. Thanks so much. And please subscribe.