final report database systems homes com database systems for boston house price prediction

Realizing this, we decided to work on house price prediction in Boston.. A house-price prediction model can provide numerous benefits to home purchasers, property investors, andhome buil

FINAL REPORT Database Systems

Homes.com: Database Systems for Boston House

Price Prediction Lectuter: Cu Nguyen Giap Class: INS 205502

Vu Thi Tra My

Nguyen Chi Nghia

Tran Thanh Nhan

Table Contents

Member whose contributions each member of the group :

Write report Collect information Bussiness nerratives Relational schema Retrieving the database Make 10 questions

Slide ERD Relational schema Retrieving the database Insert real sample data Make 10 questions

Slide Bussiness nerratives ERD

Create physical database Inserting real sample data Make 10 questions

I. Introduction:

1 About Company:

Homes.com is the fastest growing home search website in the

industry That’s because they’ve built a site that both agents and

homebuyers love A place where agents are empowered to grow their

business and provide best in class service without giving away their

commissions A place where homebuyers can connect directly with the

agents that know the home best and work with the agent of their choice

About search, content, and advertising strategies are designed to bring millions of

transaction-ready buyers and sellers to Homes.com, where they can find a great agent, or connect to their current one and collaborate during the entire process Homes.com offer a full line of advertising products and online marketing services designed to help real estate professionals connect with interested buyers and sellers If your goals include connecting with quality buyers and sellers searching for their next home and leveraging the right tools and services to grow your business, you’ve come to the right place It has tons of resources to help you stay informed of what’s happening in the industry, what’s working for successful agents, and what tactics are leading to success in today’s market

2. Bussiness Nerratives

Housing is one of the most basic demands of human life, along with food, water, and other necessities As people's living circumstances improved, demand for housing increased rapidly Housing markets have a favorable impact on a country's currency, which is a significant factor in the national economy Realizing this, we decided to work on house price prediction in Boston A house-price prediction model can provide numerous benefits to home purchasers, property investors, and home builders This model may provide a wealth of information and expertise to home purchasers, property investors, and home builders, such as the valuation of current market house prices, which will assist them in determining house pricing Meanwhile, this model can assist potential purchasers in determining the features of a property that are appropriate for their budget

In this project, we will develop and evaluate the performance and the predictive power of a model trained and tested on data collected from houses in Boston’s suburbs Once we get a good fit,

we will use this model to predict the monetary value of a house located at the Boston’s area A model like this would be very valuable for a real state agent who could make use of the information provided

II. Data dictionary:

The dataset used in this project comes from the UCI Machine Learning Repository This data was collected in 1978 and each of the 506 entries represents aggregate information about 13 features

of homes from various suburbs located in Boston and some data about buyer, seller and investor information is generated by us from collecting information on the internet And here is 13 detailed attribute information can be found below:

Attribute Information:

- CRIM: Per capita crime rate by town

- ZN: Proportion of residential land zoned for lots over 25,000 sq.ft

- INDUS: Proportion of non-retail business acres per town

- CHAS: Charles River dummy variable (= 1 if tract bounds river; 0 otherwise)

- NOX: Nitric Oxide concentration (parts per 10 million)

- RM: The average number of rooms per dwelling

- AGE: Proportion of owner-occupied units built before 1940

- DIS: Weighted distances to five Boston employment centers

- RAD: Index of accessibility to radial highways

- TAX: Full-value property-tax rate per 10,000 dollars

- PTRATIO: Pupil-teacher ratio by town

- LSTAT: % lower status of the population

- MEDV: Median value of owner-occupied homes in 1000 dollars

III. Analyzing and draw the ERD diagram

The entity relationship diagram can be thought of as the database's design sketch ERD provides visualization for database design, hence it serves the following functions:

- Supports in the definition of information system requirements across the organization and assists users in planning how to organize data It facilitates planning before beginning to build the tables

- The ERD diagram can be used as a document to help others comprehend the database's core

- Once the relational database has been deployed, the ERD can still be used as a reference point if the debug or business process needs to be re-established later

Analyzing the entities:

+ Property: the property table includes the address, number of floors, year of construction, area

of 1 property, and the ID attached to each property

+ Person: the person table plays the role of managing the properties, through the propertyID and it is divided into 3 main categories (Seller, Customer, Investor) through the ID of the table role + Roles: role table for information about types of people (Seller, Customer, Investor) + Status: status table to view the status of the property based on the ID of the table status (sold,

on sale, fixing)

+ HousePrices: house price list for sale date and original selling price of the property via propertyID

+ MarketData: provides information about the real estate market by address and date and at each time there is a main keyword, MarketDataID

+ Prediction: provide the property's predicted date and price via the propertyID and the MarketData table influenced prediction table via the MarketDataID

+ PropertyType: this table for found property's classification (Villa, Apartment, Cabin, Penthouse) via TypeID

+ Interior: this table shows the interior of each property based on the PropertyID

IV. Relational Schema:

The Relational Schema is generated from the ERD, displaying the table elements that correspond

to the entities and providing table designers in SQL server with a more detailed perspective of the table implementation

V. Build a database using SQL Sever

1 Create Database:

- Create and Using database:

Create DataBase CREATE DATABASE BostonHousePricePredictionDB GO

Use Database USE BostonHousePricePredictionDB GO

- Create table Role

Create table Role 1 CREATE TABLE Role (

roleID int IDENTITY(1 1, ) NOT NULL, set roleID as PK roleName varchar(30) NOT NULL,

CONSTRAINT [PK_Role] PRIMARY KEY CLUSTERED set roleID as PK (

roleID ASC

)WITH PAD_INDEX ( = OFF, STATISTICS_NORECOMPUTE = OFF, IGNORE_DUP_KEY

= OFF, ALLOW_ROW_LOCKS = ON, ALLOW_PAGE_LOCKS = ON) ON [PRIMARY]); GO

- Create table Status

Create table Status 2 CREATE TABLE Status (

statusID int IDENTITY(, ) NOT NULL, set statusID as PK statusName varchar(30) NOT NULL,

CONSTRAINT [PK_Status] PRIMARY KEY CLUSTERED set personID as PK (

statusID ASC

)WITH PAD_INDEX ( = OFF, STATISTICS_NORECOMPUTE = OFF, IGNORE_DUP_KEY

= OFF, ALLOW_ROW_LOCKS = ON, ALLOW_PAGE_LOCKS = ON) ON [PRIMARY]

);

GO

- Create table PropertyType 3

Create table PropertyType 3 CREATE TABLE PropertyType (

typeID int IDENTITY(1 1, ) NOT NULL, set typeID as PK typeName varchar(30) NOT NULL

CONSTRAINT [PK_PropertyType] PRIMARY KEY CLUSTERED set personID as PK

(typeID ASC

)WITH PAD_INDEX ( = OFF, STATISTICS_NORECOMPUTE = OFF, IGNORE_DUP_KEY

= OFF, ALLOW_ROW_LOCKS = ON, ALLOW_PAGE_LOCKS = ON) ON [PRIMARY]

GO

- Create table MarketData 4

Create table MartketData 4 CREATE TABLE MarketData (

marketDataID int IDENTITY( ,) NOT NULL,

date date NOT NULL,

address varchar(100) NOT NULL,

CRIM float NOT NULL,

ZN float NOT NULL,

INDUS float NOT NULL,

CHAS bit NOT NULL,

NOX float NOT NULL,

RM float NOT NULL,

AGE float NOT NULL,

DIS float NOT NULL,

RAD int NOT NULL,

TAX int NOT NULL,

PTRATIO float NOT NULL,

LSTAT float NOT NULL,

MEDV float NOT NULL,

CONSTRAINT [PK_MarketData] PRIMARY KEY CLUSTERED set marketDataID as PK

(marketDataID ASC

)WITH PAD_INDEX ( = OFF, STATISTICS_NORECOMPUTE = OFF,

IGNORE_DUP_KEY

= OFF, ALLOW_ROW_LOCKS = ON, ALLOW_PAGE_LOCKS = ON) ON [PRIMARY]

);

GO

- Create table Person

Create table Person 5 CREATE TABLE Person (

personID int IDENTITY( , ) NOT NULL,

name varchar(50) NOT NULL,

phone varchar(11) NOT NULL,

address varchar(100) NOT NULL,

gender bit NOT NULL,

roleID int FOREIGN KEY REFERENCES Role(roleID) NOT NULL,

CONSTRAINT [PK_Person] PRIMARY KEY CLUSTERED set personID as

PK (personID ASC

)WITH PAD_INDEX ( = OFF, STATISTICS_NORECOMPUTE = OFF,

= OFF, ALLOW_ROW_LOCKS = ON, ALLOW_PAGE_LOCKS = ON) ON [PRIMARY]

);

GO

- Create table Property

Create table Property 6 CREATE TABLE Property (

propertyID int IDENTITY( ,) NOT NULL, set propertyID as PK squareFootage decimal(10, ) NOT NULL,

floor int NOT NULL,

yearBuilt date NOT NULL,

address varchar(100) NOT NULL,

saleDate date NOT NULL,

salePrice decimal( ,) NOT NULL,

typeID int FOREIGN KEY REFERENCES PropertyType typeID( ) NOT NULL,

statusID int FOREIGN KEY REFERENCES Status(statusID) NOT NULL,

personID int FOREIGN KEY REFERENCES Person personID( ) NOT NULL,

CONSTRAINT [PK_Property] PRIMARY KEY CLUSTERED set personID as PK ( propertyID ASC

)WITH PAD_INDEX ( = OFF, STATISTICS_NORECOMPUTE = OFF, IGNORE_DUP_KEY

= OFF, ALLOW_ROW_LOCKS = ON, ALLOW_PAGE_LOCKS = ON) ON [PRIMARY]

);

GO

- Create table Interior

Create table Interior 7 CREATE TABLE Interior (

propertyID int IDENTITY(1 1, ) NOT NULL, set propertyID as PK numbedRooms int NOT NULL,

numBathrooms int NOT NULL,

kitchen bit NOT NULL,

pool bit NOT NULL,

garden bit NOT NULL,

garage bit NOT NULL,

CONSTRAINT FK_Property FOREIGN KEY (propertyID) REFERENCES Property propertyID( ), set propertyID as FK for table Property(propertyID)

CONSTRAINT [PK_Interior] PRIMARY KEY CLUSTERED set personID as PK (

propertyID ASC

)WITH PAD_INDEX ( = OFF, STATISTICS_NORECOMPUTE = OFF, IGNORE_DUP_KEY

= OFF, ALLOW_ROW_LOCKS = ON, ALLOW_PAGE_LOCKS = ON) ON [PRIMARY]

);

GO

- Create table Prediction

Create table Prediction 8 CREATE TABLE Prediction (

propertyID int NOT NULL, set propertyID as PK marketDataID int NOT NULL set marketDataID as PK CONSTRAINT FK_MarketData FOREIGN KEY (marketDataID) REFERENCES MarketData marketDataID( ), set marketDataID as FK for table MarketData(marktDataID),

CONSTRAINT FK_Prediction FOREIGN KEY (propertyID) REFERENCES Property propertyID( ), set marketDataID as FK for table MarketData(marktDataID),

predictionDate date NOT NULL,

predictionPrice decimal( ,) NOT NULL,

CONSTRAINT [PK_Perediction] PRIMARY KEY CLUSTERED set personID as PK (

propertyID, marketDataID ASC

)WITH PAD_INDEX ( = OFF, STATISTICS_NORECOMPUTE = OFF, IGNORE_DUP_KEY

= OFF, ALLOW_ROW_LOCKS = ON, ALLOW_PAGE_LOCKS = ON) ON [PRIMARY]

);

GO

2 Insert Table :

- INSERT INTO Role

INSERT INTO Role VALUES('Seller');

INSERT INTO Role VALUES('Customer');

INSERT INTO Role VALUES('Investor');

GO

- INSERT INTO Status

INSERT INTO Status VALUES('On Sale');

INSERT INTO Status VALUES('Sold');

INSERT INTO Status VALUES('Fixing');

GO

- INSERT INTO PropertyType

INSERT INTO PropertyType VALUES('Villa');

INSERT INTO PropertyType VALUES('Cabin');

INSERT INTO PropertyType VALUES('Apartment');

- INSERT INTO Person

INSERT INTO Person VALUES('Andrew Garfield', '08172645781', '408 5th Ave, Brooklyn, United States', , 0 1);

INSERT INTO Person VALUES('Charlie Puth', '04716284625', '3548 S Jefferson St #52, Falls Church, United States', , 0 2);

INSERT INTO Person VALUES('Selena Gomez', '0751827458', '1455 S Lamb Blvd, Las Vegas, United States', , 0 3);

- INSERT INTO Property

INSERT INTO Property VALUES(5000, , 2 '04/11/2018', '974 Blue Hill Avenue, Boston, United States', '01/09/2023', 3012, , , ); 1 1 1 INSERT INTO Property VALUES(3670, , 1 '06/20/2012', '521 Washington

St, Boston, United States', '10/10/2019', 5921, , , 1 1 1);

INSERT INTO Property VALUES(2830, , 3 '07/10/2015', '415 American Legion Hwy, Boston, United States', '01/07/2020', 5921, , , 2 2 1);

- INSERT INTO Interior

INSERT INTO Interior VALUES( , , , , , );3 2 1 1 0 1 INSERT INTO Interior VALUES( , , , , , );2 2 1 0 0 0 INSERT INTO Interior VALUES( , , , , , );3 1 1 0 1 1 INSERT INTO Interior VALUES( , , , , , );1 1 1 1 0 0 INSERT INTO Interior VALUES( , , , , , );2 2 1 0 1 1

- INSERT INTO MarketData

INSERT INTO MarketData VALUES('01/01/2023','Blue Hill Avenue, Boston, United States', 0.00632, 18, 2.31, , 0 0.538, 6.575, 65.2, 4.09, ,1

296, 15.3, 4.98, 24);

INSERT INTO MarketData VALUES('03/01/2023','Blue Hill Avenue, Boston, United States', 0.02731, , 0 7.07, , 0 0.469, 6.421, 78.9, 4.9671, ,2

242, 17.8, 9.14, 21.6);

INSERT INTO MarketData VALUES('06/01/2023','Blue Hill Avenue, Boston, United States', 0.02729, , 0 7.07, , 0 0.469, 7.185, 61.1, 4.9671, ,2

242, 17.8, 4.03, 34.7);

- INSERT INTO Prediction

INSERT INTO Prediction VALUES( , ,1 1'01/01/2023', 9992.213);

INSERT INTO Prediction VALUES( , ,1 2'03/01/2023', 10000.324);

INSERT INTO Prediction VALUES( , ,1 3'06/01/2023', 8823.534);

VI Business Questions:

1 Display all houses in Boston ?

SELECT *

FROM Property

Output:

2 Print out the houses with the smallest principal amount?

SELECT *

FROM Property WHERE salePrice = (SELECT MIN(salePrice) FROM Property)

Output:

3 Print out houses located in the Adams ?

SELECT FROM * Property

Output:

4 Find and print the homes with the lowest principal on Blue Hill Avenue ?

SELECT FROM * Property WHERE Property.address like '%'+'Blue Hill Avenue'+'%' ORDER BY Property salePrice

Output:

5 Find 10 homes with the lowest predicted price in June 2023?

SELECT TOP 10 *FROM Property

INNER JOIN Prediction ON Property.propertyID = Prediction.propertyID WHERE MONTH(predictionDate) = 6

ORDER BY predictionPrice

Output:

6 Find the 10 homes with the lowest predicted prices between

September and December 2023?

SELECT * FROM Property

INNER JOIN Prediction ON Property.propertyID =

Prediction.propertyID WHERE MONTH(predictionDate) BETWEEN 9 AND

12 ORDER BY predictionPrice

Output:

7 Find homes by status?

CREATE PROCEDURE PropertyStatus @statusName nvarchar(30) AS

SELECT *

FROM Property

INNER JOIN Status ON Property.statusID = Status.statusID

WHERE Status.statusName = @statusName EXEC PropertyStatus @statusName = 'On Sale'

Output:

8 Print out the predicted prices of homes currently for sale in September?

SELECT * FROM Property

INNER JOIN Status ON Status.statusID = Property statusID

INNER JOIN Prediction ON Prediction.propertyID = Property.propertyID WHERE Status.statusID = 1 AND MONTH(Prediction predictionDate ) = 9

Output: