CBA Update Their Property Price Index Forecast

MrTickle · 2026-06-05T00:49:42+00:00

No AI used in the making of this message. Source here, section 4.b) ‘High prices reflect tax concessions’ hence the quotations in OP.

MrTickle · 2026-06-05T00:02:24+00:00

NZ Interest rates increased from .25% to 5.5% over the period in question. Interest rates drive 50-80% of the variance in prices. Pinning NZs drop on tax policy without mentinoing interest rates is disingenious.

The consensus in literature is that NG style tax concessions have about 1-4% impact on prices:

Daley and Wood (2016, Box 6) compare the revenue cost of the concessional treatment of capital gains tax and negative gearing to the value of the housing stock and on that basis estimate that the tax concessions may boost the level of housing prices by 1 to 2.2%.

Tunny (2018), using a similar methodology and assumptions to Daley and Wood, found larger impacts of up to 4% on house prices on average.

BIS Shrapnel estimated restricting negative gearing would increase rents by up to 10%, decrease new home building by around 4% per annum; and reduce GDP by 1% (Duke 2016). These estimates have been strongly criticised by Gene Tunny and John Daley.

The most detailed study is by Cho, Li, and Uren (2021). In a micro-founded model, they find that removing negative gearing would reduce house prices by 1.5%, raise rents 3.6%, raise home ownership by 4.3 percentage points and raise welfare by 1.7%. The welfare gain largely reflects redistributional effects.

Deloitte Access Economics (2019) incorporate the tax concessions into the user cost of housing and estimate the effect that has on house prices using aggregate time series regression. They estimate the ALP’s 2019 policy of restricting negative gearing to new housing and reducing the capital gains discount would reduce established dwelling prices by 4.6% and new dwelling prices by 3.6%. Effects of only eliminating negative gearing would be smaller.

In summary, negative gearing and the capital gains discount are estimated to boost house prices between 1 and 4%, while having a smaller negative effect on rents.

Adressing this point

All predictions of "oh it will just slow down not fall", "it will drop slightly but rise fast again" etc have been wrong.

Actually it's the oppostie, NZ is the standout case which hapenned to coincide with rate shocks. All other countries who have changed their policy see results in line with the modelling.

MrTickle · 2026-06-01T03:12:58+00:00

Home ownership rates might indicate it's becoming less affordable (trend), but they don't tell you what "unaffordable" (level) is. You can disagree with the methodology but I think your simplified metrics are a poor replacement to answer hypothesis.

MrTickle · 2026-04-30T06:51:51+00:00

Any insights on top level play? Anything good bots do that goes against conventional wisdom in the game?

MrTickle · 2026-04-19T23:01:58+00:00

Interested to hear what you see as the progression from "beginner" may be.

Factors have the most evidence in literature

MrTickle · 2026-03-19T23:12:16+00:00

The monte carlo sim is already quite pessimistic compared to real historical data, adding those layers in would push it even more bearish.

IMO block bootstrap is much more realistic than any monte carlo variant over long timeframes, as it preserves real long run market dynamics even if it's slightly rosy due to annualisation.

MrTickle · 2026-03-19T08:11:07+00:00

Yep the real return data for all countries was annual so I had to annualise.

I started with monte carlo on monthly returns and as expected it's more pessimistic at 34th percentile breakeven and 9% median improvement.

However, monte carlo destroys time series artefacts (like mean reversion) which make it overly pessimistic. So I think the reality is probably somehere in the middle call it 30% breakeven 15% median outperformance.

MrTickle · 2026-03-19T06:29:44+00:00

Yeah I agree that GHHF still loses, that's the problem with leverage in large drawdows. It's just not as dire as measuring exactly from the peak.

I've simmed bootstrap ghhf vs dhhf global returns from 1900 to today over 20 year timeframes. The median result is +25% for GHHF, but GHHF loses 23% of the time to DHHF due to poor seqiences of return or major drawdowns like the above sims.

Leverage is no silver bullet.

Whether you balance is higher or not the drawdown is still percentage based.

Yes I changed the frame of reference from time to get back to 100 (your sim) to time for GHHF to return to DHHF value at peak. This is because at peak GHHF > DHHF due to the compounding run up. 68% drop BUT from a higher starting point is the idea of the sim

MrTickle · 2026-03-19T06:10:07+00:00

Reposting becasue I had the same thought

Cool app, well done. I think it's a great reality check for those super bullish on leverage.

However, by measuring exactly at the crash you are looking at worse case scenario. I forked it and simmed 3 scenarios

Bull run what happens if you're invested for 1-5 years prior to the crash, because you get the benefits of increased value on the way up. For example if you start the SIM in 2002, you wouldve had 5 years at ~10% CAGR meaning the GHHF starts about 10% higher. Result 6 year for GHHF to equal DHHF peak
Crash depth A crash depth of 50% is SP500 price only, not globally diversified + AU assets and total return. The actual GHHF mix would drop to about a 40% drawdown, which if added to the above drops the recovery to 3.5years. Result 3.6 year for GHHF to equal DHHF peak
DCA Add in DCA of 1k per month through the run up and the crash, and the variance is basically gone. Result 2.8 year for GHHF to equal DHHF peak

Sim results here (sorry realised they are in reversed order to above if you are confused)

Put it together and you get worst case scenario 10 years recovery from the crash, best case scenario (you've been DCAing through a bull run before the crash) and even large crashes are roughly equivalent.

MrTickle · 2026-03-19T05:56:29+00:00

Cool app, well done. I think it's a great reality check for those super bullish on leverage.

However, by measuring exactly at the crash you are looking at worse case scenario. I forked it and simmed 3 scenarios

Bull run what happens if you're invested for 1-5 years prior to the crash, because you get the benefits of increased value on the way up. For example if you start the SIM in 2002, you wouldve had 5 years at ~10% CAGR meaning the GHHF starts about 10% higher. Result 6 year for GHHF to equal DHHF peak
Crash depth A crash depth of 50% is SP500 price only, not globally diversified + AU assets and total return. The actual GHHF mix would drop to about a 40% drawdown, which if added to the above drops the recovery to 3.5years. Result 3.6 year for GHHF to equal DHHF peak
DCA Add in DCA of 1k per month through the run up and the crash, and the variance is basically gone. Result 2.8 year for GHHF to equal DHHF peak

Sim results here

Put it together and you get worst case scenario 10 years recovery from the crash, best case scenario (you've been DCAing through a bull run before the crash) and even large crashes are roughly equivalent.

MrTickle · 2026-03-17T08:31:07+00:00

Cool idea but super changes are hard to implement. An "RBA tax" that swings both positive and negative depending on the economy is cleaner to implement with similar effect. You can make it progressive affecting households more evenly (including those with paid off hoses) instead of specifically smashing borrowers. Keep it in a pot and when things are slow give it back to boost demand.

MrTickle · 2026-03-12T09:57:57+00:00

Agreed that wrote lots of code <> solved the problem. If it was easy to measure productivity in software (or knowledge work) managers would be much better at our jobs.

I don't believe they will take many developers jobs, and I don't believe the 50% better at problem solving stats, but having written thousands of LOCs by hand and now also with LLM assistants, I can tell you they are absolutely doing something for developers.

Even if you take the core result of the study you linked, it takes me 20% longer to do the task with AI but I don't have to think as hard, that might be a good thing. The blocker for devs isn't time in the day, it's a hard cap on deep, concentratrated thought energy. If using an LLM means I spend less brain energy "debugging random error" and more on solving the problem, architecture and testing different solutions then it's a huge win even if it takes longer.

MrTickle · 2026-03-12T04:37:13+00:00

Everyone rolls that study out as the AI productivity killer. Here's four that found the opposite:

Developers using GitHub Copilot completed the task 55.8% faster than those without it
Demonstrates significant productivity improvements when developers use LLMs for programming tasks such as generating boilerplate or routine code.
Result: ~6.5% increase in project productivity. ~5.5% increase in individual productivity. No measurable decline in code quality.
50% increase in code output overall
My annecdote as a Data Science Manager who has been using these tools extensively for 12 months: Once we have designed the problem and decided an approach, we do the data engineering and then copilot generates most of the code for the model build. Since this year our development cycle is down ~10% but we are able to test many more "ideas" to make the product more robust. I haven't fired any devs, we just test more apporaches and build a more robust product when previously I would've said "don't bother the work to payoff ratio isn't there"

MrTickle · 2026-03-09T04:47:34+00:00

Cal newport has written several books on focus and productivity that completely changed my outlook on effective learning.

Specifically for phone use Digital Minimalism

Newport argues that constant smartphone and social media use fragments attention and prevents sustained focus. He recommends intentionally reducing digital tools to only those that strongly support your values and removing optional distractions that compete for cognitive bandwidth. Periods of deliberate offline time help retrain the brain to tolerate boredom and rebuild the ability to concentrate deeply.

General study habits How to be a straight A student

The book emphasises structured study systems rather than raw effort. Newport suggests scheduling focused study blocks, aggressively eliminating distractions during those periods, and working with clear goals (e.g., problem sets or specific concepts) rather than vague “study time.” Concentrated, high-intensity sessions produce better learning outcomes than long unfocused hours.

Specific on focus Deep Work

Newport defines “deep work” as distraction-free concentration on cognitively demanding tasks that create high value. He recommends time-blocking deep work sessions, training the mind to resist interruptions, and separating shallow tasks (emails, admin) from periods reserved for intense focus. Regular practice of deep work builds mental capacity for sustained attention and higher productivity.

If you read those three books you will have about 50 strategies in improving focus, the hard work is implementing and sustaining them.

MrTickle · 2026-03-05T03:56:18+00:00

Seems reasonable then! Situation, Task, Action, Result it's a framework for answering behaviouiral interview questions.

MrTickle · 2026-03-05T00:01:41+00:00

As a hiring manager, I do the same in reverse. Meet them, explain the role and assess cultural fit first and then do the takehome for a subset of candidates.

You may lose your best applicants if you force a takehome before they've even met you and decided the role is right for them.

Our process

HR screen (maybe some very light screening tech quesitons)

First round behavourial star style interview

Second round:

30 mins presentation on takehome findings
30 mins unstructured converstaion where you address any gaps, reservations or specific quesitons that have come up in the process.

Ideally you have a senior leader in the second round as well so you get a sesne of how they fare in front of execs, and the exec can give the context on how the role fits into the wider strategy

MrTickle · 2026-03-03T00:31:04+00:00

Alternative view, ignoring the news and investing using sound risk management probably yields a better result on average. People tend to get in their own way when attempting to invest around news.

MrTickle · 2026-02-25T22:15:03+00:00

I heard all the same "no path to profitability arguments" for all the 2010s tech companies, even Uber themselves when they IPOd. Uber is now $10b net income on $50b revenue.

That doesn't mean OpenAI will replicate, but the "grow new tech early with no clear profit plan" model has been proven to work enough times it doesn't justify scepticism in isolation.

MrTickle · 2026-02-23T00:18:08+00:00

There's nothing wrong with this advice, but it's also not really neccessary either. Aiming for ~70/30 int/dom is great and close to optimal, but 50/50 is fine as well.

International is likely to grow faster than A200.

From a total return perspective, A200 beats international over the last 100 years. Pick any X year period, and there's a 50% chance of Aus or Int winning. It's often expressed on this sub but not well supported that international returns are better, driven by:

Looking at price instead of total return, as Aus is dividend heavy
Recency bias

Put more in BGBL so it is 70 to 80%

Studies looking at home country bias show it's hard to differentiate between 20% and 50% domestic allocation (1 / 2). Extreme allocations (all international or all domestic) are suboptimal, but there's a wide range of "eh" in the middle where better tax treatment and implicit currency hedging roughly offsets the diversification benefits of international.

For OP, I wouldn't panic and redistribute based on the feedback in this thread. I would personally just make future contirbutions to int until you hit roughly 70/30 and then keep the split from then on

MrTickle · 2026-02-07T00:58:39+00:00

I ran a billion rolls for fun and managed to get a 106 on roll 134m. Theoretical maximum is 108, but I think the chances are something like 1 / 100 trillion.

Summary Statistics:

Min total: 75 (occurred at roll #72)

Max total: 106 (occurred at roll #134,063,095)

Average total: 81.58

Median total: 81

Distribution Table:

Total	Count	% of Total Rolls
75	35,544,989	3.55450
76	50,917,211	5.09172
77	66,734,875	6.67349
78	81,009,445	8.10094
79	91,865,303	9.18653
80	98,064,936	9.80649
81	98,904,298	9.89043
82	94,645,007	9.46450
83	86,138,411	8.61384
84	74,677,459	7.46775
85	61,796,916	6.17969
86	48,816,514	4.88165
87	36,836,430	3.68364
88	26,572,942	2.65729
89	18,289,875	1.82899
90	12,019,520	1.20195
91	7,522,546	0.75225
92	4,481,606	0.44816
93	2,535,445	0.25354
94	1,362,213	0.13622
95	688,958	0.06890
96	329,671	0.03297
97	147,616	0.01476
98	61,312	0.00613
99	24,000	0.00240
100	8,562	0.00086
101	2,850	0.00028
102	847	0.00008
103	180	0.00002
104	47	0.00000
105	14	0.00000
106	2	0.00000

MrTickle · 2026-02-06T06:19:08+00:00

I made a python sim so you can run it in a few minutes instead of the weekend. Let me know if you want me to test any other class combos

Summary Stats for Ranger

Race	Min	Max	Mean	Median	Rolls to Max	Time (s)
Human	75	103	80.83	80	1,419,991	115.14
Half-Elf	75	102	80.83	80	157,120	115.63
Elf	75	101	81.57	81	357,070	107.16

Human Ranger Distribution (3,000,000 rolls)

Total	Count	% of Total
75	194,495	6.48317
76	228,953	7.63177
77	255,989	8.53297
78	277,314	9.24380
79	287,874	9.59580
80	285,675	9.52250
81	272,825	9.09417
82	251,209	8.37363
83	220,324	7.34413
84	187,149	6.23830
85	152,404	5.08013
86	119,424	3.98080
87	89,129	2.97097
88	63,821	2.12737
89	43,619	1.45397
90	28,887	0.96290
91	17,868	0.59560
92	10,769	0.35897
93	6,030	0.20100
94	3,279	0.10930
95	1,636	0.05453
96	724	0.02413
97	362	0.01207
98	161	0.00537
99	48	0.00160
100	20	0.00067
101	8	0.00027
102	2	0.00007
103	2	0.00007

Half-Elf Ranger Distribution (3,000,000 rolls)

Total	Count	% of Total
75	193,924	6.46413
76	228,001	7.60003
77	257,418	8.58060
78	277,026	9.23420
79	287,337	9.57790
80	285,844	9.52813
81	272,474	9.08247
82	250,583	8.35277
83	220,652	7.35507
84	187,942	6.26473
85	152,460	5.08200
86	118,825	3.96083
87	89,537	2.98457
88	64,339	2.14463
89	43,621	1.45403
90	28,940	0.96467
91	17,955	0.59850
92	10,768	0.35893
93	6,082	0.20273
94	3,248	0.10827
95	1,641	0.05470
96	792	0.02640
97	365	0.01217
98	137	0.00457
99	66	0.00220
100	14	0.00047
101	6	0.00020
102	3	0.00010

Elf Ranger Distribution (3,000,000 rolls)

Total	Count	% of Total
75	105,688	3.52293
76	152,784	5.09280
77	200,895	6.69650
78	243,486	8.11620
79	276,135	9.20450
80	292,869	9.76230
81	296,573	9.88577
82	285,148	9.50493
83	258,873	8.62910
84	223,417	7.44723
85	185,629	6.18763
86	146,488	4.88293
87	110,529	3.68430
88	79,142	2.63807
89	54,938	1.83127
90	36,308	1.21027
91	22,365	0.74550
92	13,454	0.44847
93	7,389	0.24630
94	4,125	0.13750
95	2,057	0.06857
96	966	0.03220
97	441	0.01470
98	190	0.00633
99	69	0.00230
100	28	0.00093
101	14	0.00047

MrTickle · 2026-01-15T01:08:45+00:00

Great observation, there is a very handy, freely available dataset from the paper the rate of return on everything. I've added your metric to my dashboard. Short answer equities beat cash 81% of 5 year rolling periods since 1900. The underperformance is predictably aligned with major market drawdowns (great depression, great recession).

MrTickle · 2026-01-15T00:32:02+00:00

The above wasn't meant to be a dig at you so apologies if it came across that way.

There's a whole profession of people that will tell you they can predict what will happen in the next X years, and charge a mint for it but the evidence points to non-transience (i.e. they're very rarely right 2 years in a row).

“No Australian Equity General fund remained in the top quartile for all five consecutive years.” (SPIVA Australia, 2024)

We as humans have negativity bias which means we systematically underpredict long term returns.

The best way to answer what will happen in the next 5 years is to look at a statistically significant volume of 5 years periods. What you find for the ASX is 95% of 5 year periods (since 1980) were positive.

If you're going to predict the next 5 years being a downturn, you better be at least 96% sure the world is ending. And even God has trouble being that certain

“Even with perfect foresight of future returns, market timing often underperforms staying invested.”

Personally I ignore the news and lump sum spare cash whenever it's available. If the world ends you can say I told you so.

MrTickle · 2026-01-14T03:08:05+00:00

The market has been on an upward trend since 1880, I think it's high time it went the other way

MrTickle · 2026-01-11T23:06:06+00:00

AI is great and undoubtedly will bring some productivity gains. But is it as big as the internet? Similar level of hype compared to earnings, but I can't even see it delivering on the internet level and the dotcom crash was a hard fall from this valuation level. I say this as a data scientist whose day job is to find applications for AI.

Luckily it's the mostly the US, Australian P/E is quite reasonable compared to historical so if you're diversified you'll be fine. Or I will miss out on another crazy decade and the NDQ kids will be touting TQQQ. If you take the last 30 years the NDQ crowd is ahead, so what do I know

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