Improving the Data

Modeling Workflow

October 28, 2014

LendingHome is the most advanced

mortgage marketplace platform in the

world

What Is LendingHome?

What is LendingHome?

Simple, efficient borrower experience

Investors are matched to safe, high-yield loans

World-class mortgage ops process driven by

transparent analytics

Statistical models for credit, underwriting, pricing,

sales, marketing

LendingHome: Mortgage Marketplace

• Fastest loan funded in 72 hours

• Borrowers prequalify in 3 minutes

Investors are matched to safe, high-yield loans

• Line-item accounting for payout tracking

• Proceeds from loans wired in under a second

Looker is a data exploration solution

that operates in the database

to enable organizations to explore data

in all its detail.

What Is Looker?

Some of Looker’s Customers

LendingHome: Operations

World-class operational process driven by

transparent analytics

• Integrations with over 20 vendors

• Rigorous 96-item checklist

• Looker-driven workflow measures quality and

timing of entire process

• Highly complex reporting, dashboards are

easy in Looker

LendingHome: Ops Analytics

LendingHome: Ops Analytics

The Challenge

Data scientists create value by creating

actionable models

More time spend preparing data and evaluating

results than using heavy data science

techniques

How to speed up analytical cycles

Focusing on Expertise

Define

Goals

Data Cleaning

and Shaping

Model

Dev

Expertise

“Work”

Model

Eval

Our Example

38 million flights between 2000 and 2005

Carrier information, departure and arrival

location, manifest data, aircraft data

Modeling on-time rates

Getting Started

Analytics as the shortcut to variable selection

Data Cleaning

Analytics can provide a distinct advantage in the

constant struggle for clean data

Data Cleaning Cont.

Selecting Variables

Pulling Data

Analytics allow data scientists to lever analytical

modeling to easily grab reshaped data

- Time-zone functions

- Sub-select functions

- Cleaned/filtered data

LendingHome: Quant

Modeling

• Credit models learned over 25M loans, 4B

payments, macroeconomic factors

• Scoring transparent to borrowers, investors

• Finds and predict borrower conversion from

180M RE transactions

• Feature extraction, data exploration, train/test

splits, model analysis in Looker

LendingHome: Factor

Analysis

LendingHome: Noisy Data

Building The Data Model

Let’s try a simple model:

Evaluating The Model

Cleaning

Analytics

Modeling

Continuing Our Example

Re-using that same analytics process, we can

explore our data more seamlessly

Examining Residuals

In-Sample vs Out-of-Sample

LendingHome: Evaluate Models

Layering Entirely New

Variables

Q & A

Try Looker on your own data.

Looker.com/trial

More Questions?

colin@looker.com

justin@lendinghome.com