Data Analytics and Visualization Workshop … · 09/04/2019 · Data Analytics • Data Visualization • Data Science • Big Data • Predictive Analytics • Machine Learning

@ApartmentWire#NMHCresearch

2019 NMHC Research ForumApril 2-3, 2019


Data Analytics and Visualization Workshop


TODAY’S WORKSHOP

• Overview• Data Analytics• Data Visualization• Questions and

Discussion

Purpose:

Provide overview and examples of approaches to data analysis and data visualization for the multifamily sector.


AUDIENCE POLL: DESCRIBE YOUR ORGANIZATION’SLEVEL OF EXPERIENCE WITH DATA VISUALIZATION

58 responses


AUDIENCE POLL: DESCRIBE YOUR ORGANIZATION’SLEVEL OF EXPERIENCE WITH DATA ANALYTICS

56 responses


WHY IS THIS WORTH AN HOUR OF MY TIME?

• CRE industry has lagged in technology adoption

• Data. More data. And more data…

• Need for actionable insights

• Need an advantage in the competitive marketplace


TERMS AND BUZZWORDS

• Data Analytics• Data Visualization• Data Science• Big Data• Predictive Analytics• Machine Learning• Business Analytics• Data Analysis• Business Intelligence

• Self-Service BI• Dashboarding• Ad-Hoc Reporting• Data Lake• Data Warehouse• OLAP• KPIs


THE BIG PICTURE

https://medium.com/@peterjaberau/big-data-science-in-5-minutes-a99372117d55

DATA VISUALIZATION


WHAT IS DATA SCIENCE?

https://towardsdatascience.com/introduction-to-statistics-e9d72d818745


DATA ANALYTICS


DATA FIRST, THEN ANALYSIS

https://www.forbes.com/sites/gilpress/2016/03/23/data-preparation-most-time-consuming-least-enjoyable-data-science-task-survey-says

Collecting and Preparing Data is

79%of the Work

What data scientists spendthe most time doing


https://www.excelcampus.com/powerquery/power-query-overview/








INDUSTRY LEADERS

Gartner “Magic Quadrant”

Analytics and BusinessIntelligence Platforms

https://www.gartner.com


SOLUTIONS

• Business intelligence dashboard

• 3rd party provider Operations Dashboard


SOLUTIONS

• Market-level revenue growth forecasting

• Machine learning, predictive analytics

• 3rd party providerYOY Revenue Change


SOLUTIONS

• Market dashboard

• Combines data from seven different metro-level data sources


SOLUTIONS

• Easy-button for updating reports

• Fuse together HUD affordability data with our portfolio data across all metros


SOLUTIONS

• Geospatial visualizations


AUDIENCE POLL: WHAT SOFTWARE TOOL IS YOUR ORGANIZATION USING FOR DATA ANALYTICS, DATA VISUALIZATION, ETC.?

41 responses


DATA VISUALIZATION & HOUSING


OVERVIEW

Goals for data visualization

Common housing stories

Tableau demo

1

2

3


THE BEST DATA VISUALIZATIONS:

• Point readers to insight in data

• Truthfully represent the information in the data

• Tell a self-contained story

• Take less than 10 seconds to understand

• Are designed agnostic of software


Sophistication

• What is the current median home value and rent?

Source: Zillow Rent Index, February 2019


Sophistication

• How much did rents increase or decrease last year?

Source: Zillow Rent Index, February 2019


Sophistication

• Should you buy or rent?

https://www.zillow.com/research/buy-rent-breakeven-horizon-19934/


DATA VISUALIZATION & HOUSING


DATA STORYTELLING & HOUSING


COMMON HOUSING STORIES

Trend

Listicle(winners and losers)

1 number X ways

Action and effect

Comparing groups/gaps

Surprise or MythbustingMyth Confirming

Sweet Spot

Outlier


TREND (TIME SERIES)

You have a story if:

• Things are going up

• Things are going down

• Things are staying the same


TREND (TIME SERIES)

Comparing across time frames:

The bubble

The Recession

The recovery


TREND (TIME SERIES)

Data viz tip:

• Use color as a narrative tool


TREND (SCATTERPLOT)

If there’s a relationship:

Don’t forget to use hedging language…


SWEETSPOT

If there’s no relationship:

Sweetspot!


SWEETSPOT IN THE WILD


COMPARING GROUPS/GAPS

Commonly compared:• Renters, Homeowners• Generations• Race• Urban, Suburban, Rural• Married, Single• Points in time


COMPARING GROUPS/GAPS

Commonly compared:• Renters, Homeowners• Generations• Race• Urban, Suburban, Rural• Married, Single

Question• Is the gap growing, shrinking

or staying the same?


LISTICLE (WINNERS & LOSERS)

In the media: • Buzzfeed• Vox’s Winners and Losers

Housing Examples: • Most affordable markets for

renters• Markets with the most renting

households


ACTION/IMPACT AND EFFECT

Examples• Current events –

Amazon HQ2 announcement


MYTHBUSTINGOR SURPRISE

Examples:• Off-campus housing is cheaper than

on-campus• Millennials don’t want to rent forever• Millennials aren’t lazy – they do

more research before making housing decisions than other generations

• Basically anything positive about Millennials

Use this kind of story to either upend something commonly believed OR if you already have a data point but don’t have a hook


MYTH CONFIRMING

Examples:• Gender inequality• Racial inequality

Back it up with data


OUTLIER

Questions:• What’s weird


1 NUMBER X WAYS

Your story:• You have a data point

How to make it interesting:• Compare it to the nation• Compare it to its historic average• Cut it by region• Cut it by race, generation, gender,

occupation


1 NUMBER X WAYS



occupation


1 NUMBER X WAYS



occupation


1 NUMBER X WAYS



occupation


1 NUMBER X WAYS


How to make it interesting:• Compare it to the nation• Compare it to its historic average• Cut it by region• Cut it by race, generation,

gender, occupation

Share of 1-2 bedroom 2018 rental listings affordable on the median teacher’s salary in King County (Seattle area) ZIP Codes


COMMON HOUSING STORIES

Trend

Listicle(winners and losers)

1 number X ways

Action and effect

Comparing groups/gaps

Surprise or MythbustingMyth Confirming

Sweet Spot

Outlier


HELPFUL QUESTIONSIs there a trend?Is there no trend?Is this a sweet spot analysisIs there a regional story?Are there winners and losers?Can I cut this data by region, income, race, tenureIs there an outlier?What’s weird? How does this compare to the nation?How does this compare to an average?How does this compare historically?What’s surprising?


GOOD DATA GRAPHICS

• Point readers to insight in data

• Truthfully represent the information in the data

• Tell a self-contained story


TODAY’S WORKSHOP

• Overview• Data Analytics• Data Visualization• Questions and

Discussion

Purpose:

Provide overview and examples of approaches to data analysis and data visualization for the multifamily sector.


QUESTIONS AND DISCUSSION

Paul Vastag

[email protected]

Elizabeth Kimn

Zillow.com/research

[email protected]

Documents

Data Analytics and Visualization Workshop … · 09/04/2019 · Data Analytics • Data Visualization • Data Science • Big Data • Predictive Analytics • Machine Learning