Property id number and cook county

The log-linear model has a lower R-squared value, but visually looks about the same when we plot the results. Maybe it will be more accurate after adding additional categories, and that's why Cook County chose to go with it. Between them, there are way too many categories. Visually inspect the categories with boxplots against the original data and against the residuals from the regressions against the lot and building square feet. If there's actually information in the categories that isn't captured by building size for example, the number of bathrooms in a house correlates pretty well with the building square feet and don't make it into the regression.

The following observations summarize interpretation of the five charts below:. An alternative to or a confirmation of the visualizations can be an F-test of a model with versus without a column. The next code compares different models via the F-test. A good F-statistic is large, which will make the p-value small, and mean that the additional variable s made a difference in the model.

Assessor's Office

The form of the result is:. The summary of the model fit, shown below, warns there's a high condition number and thus multicollinearity in the model, but I am not concerned. The log-linear model will follow the same form as the linear one. Now that the log has been taken of the square feet values, there isn't a condition number warning. Don't use these numbers because I rounded them a ton to make it easier to write, but for the linear model, a two-story house near Frank Lloyd Wright's, that's around the same size and age is about:.

The log-linear model is harder to interpret because it's multiplicative, but that's the new choice for Cook County. With this model, a two-story house near Frank Lloyd Wright's, that's around the same size and age is about:.

First, if your house is assessed below the model value, you're in a bit of a bind and will need to explain it away with other data by pointing out similarly priced houses have a pool or a better driveway than yours or something…I'm not a pro at this. But if your house is assessed above the model value, then you have math on your side. At this point, I put the model values into the data frame, wrote it to an Excel file, and used Excel to sort through the houses and highlight ones that were similar to Mom's.

The goal is to find houses that are a little better than yours building square foot, age, number of bathrooms , and still somehow are priced below yours, aided by the model.


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To run Selenium, you also need a web browser and driver interface to Python. I used Firefox and Geckodriver. Geckodriver can be installed effortlessly from conda-forge if you are using Anaconda conda install geckodriver -c conda-forge , or follow the advice on Stackoverflow without it. The rest of the dependencies are regular Python packages, listed below:.

All links in this post were accessed on or before August 21, Update September 10, : added the link to the U of C article in the references section. What's the goal?

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Fill the form in with your township, neighborhood, and property class data. Click 'Search'. There are multiple tabs with content exposed in the HTML. Are they understandable? The names are readable.

A list of URLs for similar properties: links. A list of possible columns for the model: columns. Time to get the data! Shape: , 34 Numerical columns: 22 Categorical columns: You can tell this from: The column name sometimes. Or check whether there is only one unique value, df. Check it for any strange outliers. Ignore a deprecation warning that comes from matplotlib. Out of curiosity, see what makes it an outlier: Get the PIN. Look it up online and see why it's 10x lower than any other value. It's OK that the plots are tiny because we're just looking for: Trends: Not much correlation between age and assessed value.

High correlation between the assessed value and both land area and building square feet "SqFt", "BldgSqFt". Bunching: Bunching up near a value suggests the need for a log-transformation. The land area "SqFt" has some bunching. This is interesting and may hint at of why the assessor's office went to a log-linear assesment model this year.

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Outliers: You can see Hemingway's birthplace as the outlier dot on the bottom with the low assessed value on all the charts across the top row. Also one property has very tiny "BldgSqFt". But its assessed value is above median. I looked it up, and it's just a typo. The assessment is in line with the actual size.

I'm not posting the PIN because actual humans live there. Just omit those rows. Omit the other entries since there are too few to model well. Omit that row. Entries from three columns with very few observations will be masked: "ExtConst", "ResType", and "Use". There are 10 or so columns with values that never change, which we're excluding too.

Use masks to preserve the original dataset.

- Subsequent Tax Payment System

Create masks. Without masked data, df shape: , Q: Why do this before adding the categorical variables? You can plot the categorical variables against the residuals from the first model to help identify which ones to include. R-squared: 0. Observations: AIC: Df Residuals: BIC: Variable: np. Warnings: [1] Standard Errors assume that the covariance matrix of the errors is correctly specified. The following observations summarize interpretation of the five charts below: Omit "Attic" It doesn't make intiuitive sense that the median property value seems to go down when there's a finished attic.

And a lot of the variation that was there at all disappears after the building size is accounted for. The median property value for the "None" category also stands out, but there are only four houses in it, so they'll just be lumped with "Unfinished". It makes sense. Central air was an important factor in picking Mom's house and I'd pay more for it.

What are property taxes?

Omit "External Construction" The categories are evenly distributed, but the results are hard to interpret. Actually, I tried adding this category in a model and it didn't improve the outcome see the F-tests after this section. Omit "Garage" It has 11 distinct categories, which is way too many. And "None" is apparently better than almost everything but a 3-car garage which makes no sense to me. There's not even a clear pattern of preference for attached vs. Don't include: " "Confusing—in my neighborhood Masonry killed frame. Don't include.

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