Forecasting Real Estate Value with Time Series Modeling

Picture you and your family ready to take the next step and move to a new home. Wouldn’t it be nice to know in which areas were the home prices expected to rise the most? You could make a more sound investment that way, and strengthen your family’s financial security.

Well in this post, I will be going over how to model, predict and forecast real estate value over time, using time series machine learning methods and getting a little technical. I was initially assigned a project- to pick the Top 5 ZIP codes for a real estate investment firm to make a best investment. To do this I would have to define what our “best” zip code meant, and then use those metrics to narrow down over 14,000 zip codes to 5 fit for a solid investment decision using time series modeling.

Well I’m going to spare you the methods of filtering through 14,000 ZIP codes and just focus on how we were able to forecast our top pick.

Image for post

Plot for our top pick, 15201 using Zillow Research Data, 1996–2018.

A extremely quick summary of time series modeling:

Time series modeling can be a bit tricky and confusing, so I’m going to try and sum it up quickly. The goal in time series modeling, among other common assumptions for machine learning (normal distribution, etc.) is to make your trend stationary. As that is how time series models interpret variance, you will need to de-trend your time series. There are a handful of methods for de-trending. For example, subtracting the rolling mean takes the average of however many past values you want, and subtracts that from the current observation. You will end up with spikes of values that hover around a constant mean. Another commonly used technique is called ‘differencing’, which subtracts past actual value, from a specified period of time (also called lag), by the current value.

Trends are essentially reduced until they have a constant mean, and constant variance. Essentially it boils down to ‘noise’- randomness over time.

The statsmodels SARIMA model is a very effective combination of multiple models: Auto-Regressive (AR) modeling, Moving Average (MA) modeling, and Integration (I).

#timeseries #machine-learning #time-series-forecasting #investment #real-estate

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Forecasting Real Estate Value with Time Series Modeling
Bella Garvin

Bella Garvin

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Forecasting Real Estate Value with Time Series Modeling

Picture you and your family ready to take the next step and move to a new home. Wouldn’t it be nice to know in which areas were the home prices expected to rise the most? You could make a more sound investment that way, and strengthen your family’s financial security.

Well in this post, I will be going over how to model, predict and forecast real estate value over time, using time series machine learning methods and getting a little technical. I was initially assigned a project- to pick the Top 5 ZIP codes for a real estate investment firm to make a best investment. To do this I would have to define what our “best” zip code meant, and then use those metrics to narrow down over 14,000 zip codes to 5 fit for a solid investment decision using time series modeling.

Well I’m going to spare you the methods of filtering through 14,000 ZIP codes and just focus on how we were able to forecast our top pick.

Image for post

Plot for our top pick, 15201 using Zillow Research Data, 1996–2018.

A extremely quick summary of time series modeling:

Time series modeling can be a bit tricky and confusing, so I’m going to try and sum it up quickly. The goal in time series modeling, among other common assumptions for machine learning (normal distribution, etc.) is to make your trend stationary. As that is how time series models interpret variance, you will need to de-trend your time series. There are a handful of methods for de-trending. For example, subtracting the rolling mean takes the average of however many past values you want, and subtracts that from the current observation. You will end up with spikes of values that hover around a constant mean. Another commonly used technique is called ‘differencing’, which subtracts past actual value, from a specified period of time (also called lag), by the current value.

Trends are essentially reduced until they have a constant mean, and constant variance. Essentially it boils down to ‘noise’- randomness over time.

The statsmodels SARIMA model is a very effective combination of multiple models: Auto-Regressive (AR) modeling, Moving Average (MA) modeling, and Integration (I).

#timeseries #machine-learning #time-series-forecasting #investment #real-estate

What is Time Series Forecasting?

In this article, we will be discussing an algorithm that helps us analyze past trends and lets us focus on what is to unfold next so this algorithm is time series forecasting.

What is Time Series Analysis?

In this analysis, you have one variable -TIME. A time series is a set of observations taken at a specified time usually equal in intervals. It is used to predict future value based on previously observed data points.

Here some examples where time series is used.

  1. Business forecasting
  2. Understand the past behavior
  3. Plan future
  4. Evaluate current accomplishments.

Components of time series :

  1. Trend: Let’s understand by example, let’s say in a new construction area someone open hardware store now while construction is going on people will buy hardware. but after completing construction buyers of hardware will be reduced. So for some times selling goes high and then low its called uptrend and downtrend.
  2. **Seasonality: **Every year chocolate sell goes high during the end of the year due to Christmas. This same pattern happens every year while in the trend that is not the case. Seasonality is repeating same pattern at same intervals.
  3. Irregularity: It is also called noise. When something unusual happens that affects the regularity, for example, there is a natural disaster once in many years lets say it is flooded so people buying medicine more in that period. This what no one predicted and you don’t know how many numbers of sales going to happen.
  4. Cyclic: It is basically repeating up and down movements so this means it can go more than one year so it doesn’t have fix pattern and it can happen any time and it is much harder to predict.

Stationarity of a time series:

A series is said to be “strictly stationary” if the marginal distribution of Y at time t[p(Yt)] is the same as at any other point in time. This implies that the mean, variance, and covariance of the series Yt are time-invariant.

However, a series said to be “weakly stationary” or “covariance stationary” if mean and variance are constant and covariance of two-point Cov(Y1, Y1+k)=Cov(Y2, Y2+k)=const, which depends only on lag k but do not depend on time explicitly.

#machine-learning #time-series-model #machine-learning-ai #time-series-forecasting #time-series-analysis

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