---
title: 'High Fidellity Listing Leads w/ Statistics'
disqus: hackmd
---
High Fidelity Listing Leads w/ Statistics
===
## Table of Contents
[TOC]
## Rationale
One of the largest advice starting as an entreprenuer is to start lean, and becoming smart. With the limited resources I have (money-wise) it is best to use whatever I have to create high fidelity, maximum likelihood of getting business. Listings are potentially the biggest factors of real estate success because of the 'i am the house' positioning.
To save money on marketing materials and getting higher potential leads, it is important to narrow our search based on the following criteria:
* High-turnover Rates within Normal Closing Range
* Area Code tunover rates
* Model Close Price range
* Modal $/sqft. range
* Modal Home Description (bedroom, story, carpet vs. no carpet)
* Serious Sellers
* 5 Year Equity
* Recent Divorced Couples
* Correlation to Cyclical Listing Cycle
The purpose of this research is to identify these factors on a monthly basis with a two year lookback. This will help real estate agents just starting and as well as targeting clients who need it vs. those who aren't ready. It saves marketing materials and time, at the same time, entering the market with higher confidence.
The steps we are going to take which helps us narrow our search for this year.
1. Finding Average Closing (Average Home and Price/Sqft., average everything)
2. Common Home Description
Scatter Plot Data Points based on Features
---
We need to see the data points based on features. We have to see the cycle of close, the average days until close, average everthing.
**Grab 1 Year Close Data**
*MLS > Single Family Home > History (360 days) & Active > 1 & 2 story > export Hotsheet*
### Histogram Close by `Area Code`
We can create a rationale for highest closing areas to have a lot of volume/turnover rates. Although, there are some conflicts of this conclusion. High closing home areas can mean lower supply for that particular area. But in terms of momentum, we can at least expect to capture this momentum within the past year.
We will take the top 5 areas of highest close and use an area code which is near us.
**Open File**
```gherkin=
import pandas as pd
data = pd.read_csv('file.csv')
data.tail(2)
```
**Create Histogram**
```gherkin=
data.groupby('Ar').Ar.count().sort_values(ascending=False).head(5).plot.bar();
```
### Compare 1st Story vs 2nd Story Homes
We must find which type of build homes sell more.
```gherkin=
# Take pandas data and compare
## Taking 606 Region
area606['Ar'] = data['Ar'].astype(str)
area606 = area606[area606['Ar'].str.match('606')]
#area606
print(len(area606[area606['BldgDes'].str.match('1STORY')]))
print(len(area606[area606['BldgDes'].str.match('2STORY')]))
print("1st story homes in area 606 area the most sold!")
```
### Histogram Close by `Prices` against `Time`
Make scatter plot of prices to determine cluster of closed homes from the past year. We must make a regressional plot because home prices positively correlates to time.
```gherkin=
```
Finding Average Closing Price and Price/Sqft.
---
We will create a histogram against closing price (x axis) and the amount for that group (y axis).
```gherkin=
```
> Read more about sequence-diagrams here: http://bramp.github.io/js-sequence-diagrams/
Finding Potential Areas to Market.
---
We will use 5 to 6 year data of single family homes that have only closed those years. It is expected to gain equity fairly.
**Grab Data**
*Go to MLS > Residential > Single Homes (2014-2015 history) > Download Hotsheet*
Project Timeline
---
```mermaid
gantt
title A Gantt Diagram
section Section
A task :a1, 2014-01-01, 30d
Another task :after a1 , 20d
section Another
Task in sec :2014-01-12 , 12d
anther task : 24d
```
> Read more about mermaid here: http://mermaid-js.github.io/mermaid/
## Appendix and FAQ
:::info
**Find this document incomplete?** Leave a comment!
:::
###### tags: `Templates` `Documentation`
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